Anomaly detection in deduplication pruning operations

ABSTRACT

Described herein are techniques for better understanding problems arising in an illustrative information management system, such as a data storage management system, and for issuing appropriate alerts and reporting to data management professionals. The illustrative embodiments include a number of features that detect and raise awareness of anomalies in system operations, such as in deduplication pruning operations. Such anomalies can include delays in the processing of archive files to be deleted and/or delays in the generation of the list of archive files to delete. Anomalies are characterized by frequency anomalies and/or by occurrence counts. Utilization is also of interest for certain key system resources, such as deduplication databases, CPU and memory at the storage manager, etc., without limitation. Predicting low utilization periods for these and other key resources is useful for scheduling maintenance activities without interfering with ordinary deduplication pruning operations and/or other data protection jobs.

INCORPORATION BY REFERENCE TO ANY PRIORITY APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.16/789,232 (“the '232 application”), filed Feb. 12, 2020, and entitled“ANOMALY DETECTION IN DATA PROTECTION OPERATIONS”, which is acontinuation-in-part of U.S. patent application Ser. No. 16/676,288,filed Nov. 6, 2019, and entitled “ANOMALY DETECTION IN DATA PROTECTIONOPERATIONS”, which claims the benefit of U.S. Provisional PatentApplication No. 62/899,013, filed Sep. 11, 2019, and entitled “ANOMALYDETECTION IN DATA PROTECTION OPERATIONS”, where the '232 applicationalso claims the benefit of U.S. Provisional Patent Application No.62/899,013, filed Sep. 11, 2019, and entitled “ANOMALY DETECTION IN DATAPROTECTION OPERATIONS”. Any and all applications, if any, for which aforeign or domestic priority claim is identified in the Application DataSheet of the present application are hereby incorporated by reference intheir entireties under 37 CFR 1.57.

COPYRIGHT NOTICE

A portion of the disclosure of this patent document contains materialwhich is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentand/or the patent disclosure as it appears in the United States Patentand Trademark Office patent file and/or records, but otherwise reservesall copyrights whatsoever.

BACKGROUND

Businesses recognize the commercial value of their data and seekreliable, cost-effective ways to protect the information stored on theircomputer networks while minimizing impact on productivity. A companymight back up critical computing systems such as databases, fileservers, web servers, virtual machines, and so on as part of a daily,weekly, or monthly maintenance schedule. The company may similarlyprotect computing systems used by its employees, such as those used byan accounting department, marketing department, engineering department,and so forth. Given the rapidly expanding volume of data undermanagement, companies also continue to seek innovative techniques formanaging data growth, for example by migrating data to lower-coststorage over time, reducing redundant data, pruning lower priority data,etc. Enterprises also increasingly view their stored data as a valuableasset and look for solutions that leverage their data. For instance,data analysis capabilities, information management, improved datapresentation and access features, and the like, are in increasingdemand.

SUMMARY

Described herein are techniques for better understanding problemsarising in an illustrative information management system, such as a datastorage management system, and for issuing appropriate alerts andreporting to data management professionals. For example, an informationmanagement system can track various events (e.g., user log ins, user logouts, the start of a secondary copy operation, completion of a secondarycopy operation, failure of a secondary copy operation, etc.) that occurwith respect to components of the information management system, jobstatus (e.g., the status of secondary copy operations, such as backupoperations, snapshot operations, archive operations, etc.), and/or thelike. In some cases, events occur at regular intervals and/or accordingto an expected trend. However, if the number of occurrences of an eventand/or the duration of time between an event deviates from the regularinterval or expected trend (which may be considered an anomaly), thismay indicate that a component in the information management system isfailing or failed, configured incorrectly, incompatible with othercomponents in the information management system, and/or the like. Insuch situations, computing resources (e.g., processing power, memoryusage, etc.) provided by the information management system may beoverutilized to compensate for the issue, negatively affecting theperformance of other components in the information management systemthat rely on these computing resources.

Similarly, during a specific seasonal period of time (e.g., hourly,daily, weekly, monthly, etc.), the time for a job to complete and thenumber of jobs running in an information management system that havesucceeded, are pending, have failed, have been killed, and/or aresuspended may be consistent and/or follow an expected trend. However, ifjobs start to run longer than expected and/or the number of succeeded,pending, failed, killed, and/or suspended jobs deviates from theexpected number or expected trend (which may be considered anomalies),this may indicate that a component in the information management systemis failing or failed, configured incorrectly, incompatible with othercomponents in the information management system, and/or the like. Thus,long running jobs or jobs with statuses that deviate from the norm canaffect the computing resources provided by the information managementsystem, negatively affecting the performance of other components in theinformation management system that rely on these computing resources.

In an embodiment, anomaly detection can be performed on deduplicationdatabase pruning operations. For example, an archive file may begenerated in response to a job being performed (e.g., a backup job) andthe archive file may comprise various chunks. Each chunk may store adata block and/or a reference to a data block that was already stored inanother chunk (possibly in another archive file). Information aboutarchive files and their corresponding chunks may be stored in a storagemanager database. The deduplication database may store or have access toa table that indicates, for a data block, a signature of the data block,an archive file or chunk in which the data block is stored, a referencecount of a number of times the data block is referenced by other chunksor archive files, and/or the other chunks or archive files thatreference the data block. Generally, to perform pruning of chunks, thededuplication database may receive a list of archive files that are tobe deleted, and the deduplication database can update the table toreduce the reference count as appropriate. The chunks of the archivefiles may not be deleted immediately, however, because the chunks mayinclude data blocks referenced by other chunks. Thus, the deduplicationdatabase can then, after updating the table, provide a storage managerof the information management system with a list of archive files thatonly include data blocks for which the reference count is 0. The storagemanager or another component in the information management system systemcan then delete the chunks of these listed archive files.

In some circumstances, a delay can occur in the transmission of the listof archive files that are to be deleted to the deduplication database,resulting in a backlog of archive file deletion indications that thededuplication database needs to process to update the table. In othercircumstances, the deduplication database can be running slowly suchthat there is a delay in generation of the list of archive files thatonly include data blocks for which the reference count is 0. Thus,archive files that could otherwise be deleted are not deleted, reducingthe amount of available memory space to store other blocks.

Accordingly, the storage manager can store, for various time periods, acount of the number of archive files or chunks that a deduplicationdatabase has yet to process to update the table, a count of the numberof archive files or chunks identified as only having data blocks forwhich the reference count is 0, and/or a time since the last list ofarchive files that only include data blocks for which the referencecount is 0 was generated by the deduplication database. The storagemanager can retrieve this deduplication pruning information from thededuplication database and implement the anomaly detection and reportingfunctionality described herein to detect whether there are any anomalousdelays in the pruning operations of the deduplication database. In someembodiments, instead of comparing the absolute value of the counts ortimes over various time periods, the storage manager can determine adifference in the absolute value of the counts or times between timeperiods, and use the determined differences to detect anomalies and/orgenerate alerts. In other embodiments, the storage manager uses theabsolute value of the counts or times to detect anomalies and/orgenerate alerts.

The illustrative embodiments include a number of features that detectand raise awareness of anomalies in system operations. Categories ofinterest include events and job anomalies, such as long-running jobs andjob success/failure rates. Anomalies are characterized by frequencyanomalies and/or by occurrence counts. Utilization is also of interestfor certain key system resources, such as deduplication databases, CPUand memory at the storage manager, etc., without limitation. Predictinglow utilization periods for these and other key resources is useful forscheduling maintenance activities without interfering with ordinary dataprotection jobs.

One aspect of the disclosure provides a networked information managementsystem comprising a client computing device having one or more firsthardware processors, where a first type of event occurs on the clientcomputing device. The networked information management system furthercomprises one or more computing devices in communication with the clientcomputing device, where the one or more computing devices are configuredwith computer-executable instructions that, when executed, cause the oneor more computing devices to: retrieve event data corresponding to thefirst type of event and the client computing device; perform atime-series decomposition of the event data; analyze a component of thedecomposed time-series to determine an acceptable range for a number ofoccurrences of the first type of event; determine not to expand theacceptable range in response to an indication that a number of alertsgenerated for the first type of event is less than a threshold;determine that an anomaly exists at a first time in response to adetermination that a number of occurrences of the first type of eventfalls outside the acceptable range; and generate an alert for thedetected anomaly.

The networked information management system of the preceding paragraphcan include any sub-combination of the following features: where thecomputer-executable instructions, when executed, further cause the oneor more computing devices to perform the time-series decomposition ofthe event data to form a trend component, a seasonal component, and anerror component; where the computer-executable instructions, whenexecuted, further cause the one or more computing devices to analyze theerror component to determine the acceptable range for the number ofoccurrences of the first type of event; where the computer-executableinstructions, when executed, further cause the one or more computingdevices to determine that a second anomaly exists at the first time inresponse to a determination that a duration between occurrences of thefirst type of event falls outside a second acceptable range; where theduration between occurrences of the first type of event is less than alower extreme of the second acceptable range; where the number ofoccurrences of the first type of event is greater than an upper limit ofthe acceptable range; and where the number of occurrences of the firsttype of event is less than a lower limit of the acceptable range.

Another aspect of the disclosure provides a computer-implemented methodcomprising: retrieving event data corresponding to a first type of eventthat occurs on a client computing device; performing a time-seriesdecomposition of the event data; analyzing a component of the decomposedtime-series to determine an acceptable range for a number of occurrencesof the first type of event; determining not to expand the acceptablerange in response to an indication that a number of alerts generated forthe first type of event is less than a threshold; determining that ananomaly exists at a first time in response to a determination that anumber of occurrences of the first type of event falls outside theacceptable range; and generating an alert for the detected anomaly.

The computer-implemented method of the preceding paragraph can includeany sub-combination of the following features: where performing thetime-series decomposition further comprises performing the time-seriesdecomposition of the event data to form a trend component, a seasonalcomponent, and an error component; where analyzing a component of thedecomposed time-series further comprises analyzing the error componentto determine the acceptable range for the number of occurrences of thefirst type of event; where the computer-implemented method furthercomprises determining that a second anomaly exists at the first time inresponse to a determination that a duration between occurrences of thefirst type of event falls outside a second acceptable range; where theduration between occurrences of the first type of event is less than alower extreme of the second acceptable range; where the number ofoccurrences of the first type of event is greater than an upper limit ofthe acceptable range; and where the number of occurrences of the firsttype of event is less than a lower limit of the acceptable range.

Another aspect of the disclosure provides non-transitorycomputer-readable medium storing instructions, which when executed byone or more computing devices, cause the one or more computing devicesto perform a method comprising: retrieving event data corresponding to afirst type of event that occurs on a client computing device; performinga time-series decomposition of the event data; analyzing a component ofthe decomposed time-series to determine an acceptable range for a numberof occurrences of the first type of event; determining not to expand theacceptable range in response to an indication that a number of alertsgenerated for the first type of event is less than a threshold;determining that an anomaly exists at a first time in response to adetermination that a number of occurrences of the first type of eventfalls outside the acceptable range; and generating an alert for thedetected anomaly.

The non-transitory computer-readable medium of the preceding paragraphcan include any sub-combination of the following features: where themethod further comprises performing the time-series decomposition of theevent data to form a trend component, a seasonal component, and an errorcomponent; where the method further comprises analyzing the errorcomponent to determine the acceptable range for the number ofoccurrences of the first type of event; where the method furthercomprises determining that a second anomaly exists at the first time inresponse to a determination that a duration between occurrences of thefirst type of event falls outside a second acceptable range; where theduration between occurrences of the first type of event is less than alower extreme of the second acceptable range; and where the number ofoccurrences of the first type of event is greater than an upper limit ofthe acceptable range.

Another aspect of the disclosure provides a networked informationmanagement system comprising a client computing device having one ormore first hardware processors, where the client computing device isassociated with a first job. The networked information management systemfurther comprises one or more computing devices in communication withthe client computing device, where the one or more computing devices areconfigured with computer-executable instructions that, when executed,cause the one or more computing devices to: retrieve jobs datacorresponding to the first job and the first client computing device;perform a time-series decomposition of the jobs data; analyze acomponent of the decomposed time-series to determine at least one of anacceptable range for time to perform the first job, an acceptable rangefor a size of secondary copy data associated with the first job, or anacceptable range for a number of job attempts until the first job iscomplete; determine a possible cause for the first job running longerthan the acceptable range for time to perform the first job in responseto the first job at a first time running longer than the acceptablerange for time to perform the first job; determine whether any eventscorresponding to the first job are anomalous; and generate an alert inresponse to at least one of the first job running longer or a firstevent corresponding to the first job being anomalous.

Another aspect of the disclosure provides a computer-implemented methodcomprising: retrieving jobs data corresponding to a first job and afirst client computing device; performing a time-series decomposition ofthe jobs data; analyzing a component of the decomposed time-series todetermine at least one of an acceptable range for time to perform thefirst job, an acceptable range for a size of secondary copy dataassociated with the first job, or an acceptable range fora number of jobattempts until the first job is complete; determining a possible causefor the first job running longer than the acceptable range for time toperform the first job in response to the first job at a first timerunning longer than the acceptable range for time to perform the firstjob; determining whether any events corresponding to the first job areanomalous; and generating an alert in response to at least one of thefirst job running longer or a first event corresponding to the first jobbeing anomalous.

Another aspect of the disclosure provides a non-transitorycomputer-readable medium storing instructions, which when executed byone or more computing devices, cause the one or more computing devicesto perform a method comprising: retrieving jobs data corresponding to afirst job and a first client computing device; performing a time-seriesdecomposition of the jobs data; analyzing a component of the decomposedtime-series to determine at least one of an acceptable range for time toperform the first job, an acceptable range for a size of secondary copydata associated with the first job, or an acceptable range for a numberof job attempts until the first job is complete; determining a possiblecause for the first job running longer than the acceptable range fortime to perform the first job in response to the first job at a firsttime running longer than the acceptable range for time to perform thefirst job; determining whether any events corresponding to the first jobare anomalous; and generating an alert in response to at least one ofthe first job running longer or a first event corresponding to the firstjob being anomalous.

Another aspect of the disclosure provides a networked informationmanagement system comprising one or more client computing devices eachhaving one or more first hardware processors, where the one or moreclient computing devices are associated with a plurality of jobs. Thenetworked information management system further comprises one or morecomputing devices in communication with the client computing device,where the one or more computing devices are configured withcomputer-executable instructions that, when executed, cause the one ormore computing devices to: retrieve jobs data corresponding to theplurality of jobs; perform a time-series decomposition of the jobs data;analyze a component of the decomposed time-series to determine at leastone of an acceptable range for succeeded jobs in the plurality of jobs,an acceptable range for failed jobs in the plurality of jobs, anacceptable range for killed jobs in the plurality of jobs, an acceptablerange for suspended jobs in the plurality of jobs, or an acceptablerange for pending jobs in the plurality of jobs; determine a possiblecause for at least one of the succeeded jobs, the failed jobs, thekilled jobs, the suspended jobs, or the pending jobs falling outside therespective acceptable range; generate a graph corresponding to a numberof succeeded jobs, failed jobs, killed jobs, suspended jobs, and pendingjobs; and generate an alert indicating an anomalous status of at leastone of the succeeded jobs, the failed jobs, the killed jobs, thesuspended jobs, or the pending jobs, where the alert includes thegenerated graph.

Another aspect of the disclosure provides a computer-implemented methodcomprising: retrieving jobs data corresponding to a plurality of jobs;performing a time-series decomposition of the jobs data; analyzing acomponent of the decomposed time-series to determine at least one of anacceptable range for succeeded jobs in the plurality of jobs, anacceptable range for failed jobs in the plurality of jobs, an acceptablerange for killed jobs in the plurality of jobs, an acceptable range forsuspended jobs in the plurality of jobs, or an acceptable range forpending jobs in the plurality of jobs; determining a possible cause forat least one of the succeeded jobs, the failed jobs, the killed jobs,the suspended jobs, or the pending jobs falling outside the respectiveacceptable range; generating a graph corresponding to a number ofsucceeded jobs, failed jobs, killed jobs, suspended jobs, and pendingjobs; and generating an alert indicating an anomalous status of at leastone of the succeeded jobs, the failed jobs, the killed jobs, thesuspended jobs, or the pending jobs, where the alert includes thegenerated graph.

Another aspect of the disclosure provides a non-transitorycomputer-readable medium storing instructions, which when executed byone or more computing devices, cause the one or more computing devicesto perform a method comprising: retrieving jobs data corresponding to aplurality of jobs; performing a time-series decomposition of the jobsdata; analyzing a component of the decomposed time-series to determineat least one of an acceptable range for succeeded jobs in the pluralityof jobs, an acceptable range for failed jobs in the plurality of jobs,an acceptable range for killed jobs in the plurality of jobs, anacceptable range for suspended jobs in the plurality of jobs, or anacceptable range for pending jobs in the plurality of jobs; determininga possible cause for at least one of the succeeded jobs, the failedjobs, the killed jobs, the suspended jobs, or the pending jobs fallingoutside the respective acceptable range; generating a graphcorresponding to a number of succeeded jobs, failed jobs, killed jobs,suspended jobs, and pending jobs; and generating an alert indicating ananomalous status of at least one of the succeeded jobs, the failed jobs,the killed jobs, the suspended jobs, or the pending jobs, where thealert includes the generated graph.

Another aspect of the disclosure provides a networked informationmanagement system comprising a deduplication database. The networkedinformation management system further comprises one or more computingdevices in communication with the deduplication database, where the oneor more computing devices are configured with computer-executableinstructions that, when executed, cause the one or more computingdevices to: retrieve deduplication pruning information associated withthe deduplication database; perform a time-series decomposition of thededuplication pruning information; analyze a component of the decomposedtime-series to determine an acceptable range for a time to processarchive files to be deleted; determine that an anomaly exists at a firsttime in response to a determination that a time to process archive filesto be deleted at the first time falls outside the acceptable range; andgenerate an alert for the detected anomaly.

The networked information management system of the preceding paragraphcan include any sub-combination of the following features: where thecomputer-executable instructions, when executed, further cause the oneor more computing devices to perform the time-series decomposition ofthe deduplication pruning information to form a trend component, aseasonal component, and an error component; where thecomputer-executable instructions, when executed, further cause the oneor more computing devices to analyze the error component to determinethe acceptable range for the time to process archive files to bedeleted; where the computer-executable instructions, when executed,further cause the one or more computing devices to analyze the componentof the decomposed time-series to determine a second acceptable range fora time to generate a list of archive files to delete; where theacceptable range comprises one or an absolute time value or a delta timevalue; where the time to process archive files to be deleted at thefirst time is greater than an upper limit of the acceptable range; wherethe time to process archive files to be deleted at the first time isless than a lower limit of the acceptable range; where thecomputer-executable instructions, when executed, further cause the oneor more computing devices to generate a graph indicating the anomaly fordisplay in a user interface rendered by a client computing device; wherethe computer-executable instructions, when executed, further cause theone or more computing devices to determine a possible cause of theanomaly in response to the determination that the anomaly exists; wherethe computer-executable instructions, when executed, further cause theone or more computing devices to determine a possible solution toresolve the anomaly based on the determined possible cause; and wherethe deduplication pruning information comprises at least one of a countof a number of archive files that the deduplication database has yet toprocess to update a table, a count of a number of archive filesidentified as only having data blocks for which a reference count iszero, or a time since a last list of archive files that only includedata blocks for which the reference count is zero was generated by thededuplication database.

Another aspect of the disclosure provides a computer-implemented methodcomprising: retrieving deduplication pruning information associated witha deduplication database; performing a time-series decomposition of thededuplication pruning information; analyzing a component of thedecomposed time-series to determine an acceptable range for a time toprocess archive files to be deleted; determining that an anomaly existsat a first time in response to a determination that a time to processarchive files to be deleted at the first time falls outside theacceptable range; and generating an alert for the detected anomaly.

The computer-implemented method of the preceding paragraph can includeany sub-combination of the following features: where performing atime-series decomposition further comprises performing the time-seriesdecomposition of the deduplication pruning information to form a trendcomponent, a seasonal component, and an error component; where analyzinga component of the decomposed time-series further comprises analyzingthe error component to determine the acceptable range for the time toprocess archive files to be deleted; where the computer-implementedmethod further comprises analyzing the component of the decomposedtime-series to determine a second acceptable range for a time togenerate a list of archive files to delete; where the acceptable rangecomprises one or an absolute time value or a delta time value; where thetime to process archive files to be deleted at the first time is greaterthan an upper limit of the acceptable range; where the time to processarchive files to be deleted at the first time is less than a lower limitof the acceptable range; where the computer-implemented method furthercomprises determining a possible cause of the anomaly in response to thedetermination that the anomaly exists, and determining a possiblesolution to resolve the anomaly based on the determined possible cause;and where the deduplication pruning information comprises at least oneof a count of a number of archive files that the deduplication databasehas yet to process to update a table, a count of a number of archivefiles identified as only having data blocks for which a reference countis zero, or a time since a last list of archive files that only includedata blocks for which the reference count is zero was generated by thededuplication database.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A is a block diagram illustrating an exemplary informationmanagement system.

FIG. 1B is a detailed view of a primary storage device, a secondarystorage device, and some examples of primary data and secondary copydata.

FIG. 1C is a block diagram of an exemplary information management systemincluding a storage manager, one or more data agents, and one or moremedia agents.

FIG. 1D is a block diagram illustrating a scalable informationmanagement system.

FIG. 1E illustrates certain secondary copy operations according to anexemplary storage policy.

FIGS. 1F-1H are block diagrams illustrating suitable data structuresthat may be employed by the information management system.

FIG. 2A illustrates a system and technique for synchronizing primarydata to a destination such as a failover site using secondary copy data.

FIG. 2B illustrates an information management system architectureincorporating use of a network file system (NFS) protocol forcommunicating between the primary and secondary storage subsystems.

FIG. 2C is a block diagram of an example of a highly scalable manageddata pool architecture.

FIG. 3 is a block diagram illustrating some salient portions of a systemfor detecting and reporting anomalies in the occurrence of events, thelength of jobs, and/or the status of jobs, according to an embodiment.

FIG. 4 illustrates a block diagram showing the operations performed todetect event anomalies.

FIG. 5 illustrates a block diagram showing the operations performed todetect long running jobs.

FIG. 6 illustrates a block diagram showing the operations performed todetect job statuses that are occurring too often or less often.

FIG. 7 depicts some salient operations of a method for detecting ananomalous event, according to an embodiment.

FIG. 8 depicts some salient operations of a method for detecting longrunning jobs, according to an embodiment.

FIG. 9 depicts some salient operations of a method for detectinganomalous job statuses, according to an embodiment.

FIGS. 10A-10B depict a graphical user interface showing an anomalynotification or alert, according to an embodiment.

FIG. 11A depicts another graphical user interface showing an anomalynotification or alert, according to an embodiment.

FIG. 11B depicts another graphical user interface showing an anomalynotification or alert, according to an embodiment.

FIG. 12 depicts another graphical user interface showing an anomalynotification or alert, according to an embodiment.

FIG. 13 depicts another graphical user interface showing an anomalynotification or alert, according to an embodiment.

FIG. 14 depicts another graphical user interface showing an anomaly jobdashboard, according to an embodiment.

FIG. 15 depicts some salient operations of a method for detectinganomalous delays in the pruning operations of a deduplication database,such as the deduplication database of FIG. 2C, according to anembodiment.

DETAILED DESCRIPTION

Described herein are techniques for better understanding problemsarising in an illustrative information management system, such as a datastorage management system, and for issuing appropriate alerts andreporting to data management professionals. For example, an informationmanagement system can track various events (e.g., user log ins, user logouts, user changes a setting, the start of a secondary copy operation,completion of a secondary copy operation, failure of a secondary copyoperation, service starts, service restarts, service closes, etc.) thatoccur with respect to components of the information management system,job status (e.g., the status of secondary copy operations, such asbackup operations, snapshot operations, archive operations, etc.),and/or the like. In some cases, events occur at regular intervals and/oraccording to an expected trend. However, if the number of occurrences ofan event and/or the duration of time between an event deviates from theregular interval or expected trend (which may be considered an anomaly),this may indicate that a component in the information management systemis failing or failed, configured incorrectly, incompatible with othercomponents in the information management system, and/or the like. Insuch situations, computing resources (e.g., processing power, memoryusage, etc.) provided by the information management system may beoverutilized to compensate for the issue, negatively affecting theperformance of other components in the information management systemthat rely on these computing resources.

Similarly, during a specific seasonal period of time (e.g., hourly,daily, weekly, monthly, etc.), the time for a job to complete and thenumber of jobs running in an information management system that havesucceeded, are pending, have failed, have been killed, and/or aresuspended may be consistent and/or follow an expected trend. However, ifjobs start to run longer than expected and/or the number of succeeded,pending, failed, killed, and/or suspended jobs deviates from theexpected number or expected trend (which may be considered anomalies),this may indicate that a component in the information management systemis failing or failed, configured incorrectly, incompatible with othercomponents in the information management system, and/or the like. Thus,long running jobs or jobs with statuses that deviate from the norm canaffect the computing resources provided by the information managementsystem, negatively affecting the performance of other components in theinformation management system that rely on these computing resources.

The illustrative embodiments include a number of features that detectand raise awareness of anomalies in system operations. Categories ofinterest include events and job anomalies, such as long-running jobs andjob success/failure rates. Anomalies are characterized by frequencyanomalies and/or by occurrence counts. Utilization is also of interestfor certain key system resources, such as deduplication databases, CPUand memory at the storage manager, etc., without limitation. Predictinglow utilization periods for these and other key resources is useful forscheduling maintenance activities without interfering with ordinary dataprotection jobs.

Detailed descriptions and examples of systems and methods according toone or more embodiments may be found in the sections entitled AnomalyDetection and Reporting and Anomaly Detection of Deduplication PruningOperations, as well as in the section entitled Example Embodiments, andalso in FIGS. 3 through 15 herein. Furthermore, components andfunctionality for the anomaly detection and reporting may be configuredand/or incorporated into information management systems such as thosedescribed herein in FIGS. 1A-1H and 2A-2C.

Various embodiments described herein are intimately tied to, enabled by,and would not exist except for, computer technology. For example, theanomaly detection and reporting described herein in reference to variousembodiments cannot reasonably be performed by humans alone, without thecomputer technology upon which they are implemented.

Information Management System Overview

With the increasing importance of protecting and leveraging data,organizations simply cannot risk losing critical data. Moreover, runawaydata growth and other modern realities make protecting and managing dataincreasingly difficult. There is therefore a need for efficient,powerful, and user-friendly solutions for protecting and managing dataand for smart and efficient management of data storage. Depending on thesize of the organization, there may be many data production sourceswhich are under the purview of tens, hundreds, or even thousands ofindividuals. In the past, individuals were sometimes responsible formanaging and protecting their own data, and a patchwork of hardware andsoftware point solutions may have been used in any given organization.These solutions were often provided by different vendors and had limitedor no interoperability. Certain embodiments described herein addressthese and other shortcomings of prior approaches by implementingscalable, unified, organization-wide information management, includingdata storage management.

FIG. 1A shows one such information management system 100 (or “system100”), which generally includes combinations of hardware and softwareconfigured to protect and manage data and metadata that are generatedand used by computing devices in system 100. System 100 may be referredto in some embodiments as a “storage management system” or a “datastorage management system.” System 100 performs information managementoperations, some of which may be referred to as “storage operations” or“data storage operations,” to protect and manage the data residing inand/or managed by system 100. The organization that employs system 100may be a corporation or other business entity, non-profit organization,educational institution, household, governmental agency, or the like.

Generally, the systems and associated components described herein may becompatible with and/or provide some or all of the functionality of thesystems and corresponding components described in one or more of thefollowing U.S. patents/publications and patent applications assigned toCommvault Systems, Inc., each of which is hereby incorporated byreference in its entirety herein:

-   -   U.S. Pat. No. 7,035,880, entitled “Modular Backup and Retrieval        System Used in Conjunction With a Storage Area Network”;    -   U.S. Pat. No. 7,107,298, entitled “System And Method For        Archiving Objects In An Information Store”;    -   U.S. Pat. No. 7,246,207, entitled “System and Method for        Dynamically Performing Storage Operations in a Computer        Network”;    -   U.S. Pat. No. 7,315,923, entitled “System And Method For        Combining Data Streams In Pipelined Storage Operations In A        Storage Network”;    -   U.S. Pat. No. 7,343,453, entitled “Hierarchical Systems and        Methods for Providing a Unified View of Storage Information”;    -   U.S. Pat. No. 7,395,282, entitled “Hierarchical Backup and        Retrieval System”;    -   U.S. Pat. No. 7,529,782, entitled “System and Methods for        Performing a Snapshot and for Restoring Data”;    -   U.S. Pat. No. 7,617,262, entitled “System and Methods for        Monitoring Application Data in a Data Replication System”;    -   U.S. Pat. No. 7,734,669, entitled “Managing Copies Of Data”;    -   U.S. Pat. No. 7,747,579, entitled “Metabase for Facilitating        Data Classification”;    -   U.S. Pat. No. 8,156,086, entitled “Systems And Methods For        Stored Data Verification”;    -   U.S. Pat. No. 8,170,995, entitled “Method and System for Offline        Indexing of Content and Classifying Stored Data”;    -   U.S. Pat. No. 8,230,195, entitled “System And Method For        Performing Auxiliary Storage Operations”;    -   U.S. Pat. No. 8,285,681, entitled “Data Object Store and Server        for a Cloud Storage Environment, Including Data Deduplication        and Data Management Across Multiple Cloud Storage Sites”;    -   U.S. Pat. No. 8,307,177, entitled “Systems And Methods For        Management Of Virtualization Data”;    -   U.S. Pat. No. 8,364,652, entitled “Content-Aligned, Block-Based        Deduplication”;    -   U.S. Pat. No. 8,578,120, entitled “Block-Level Single        Instancing”;    -   U.S. Pat. No. 8,954,446, entitled “Client-Side Repository in a        Networked Deduplicated Storage System”;    -   U.S. Pat. No. 9,020,900, entitled “Distributed Deduplicated        Storage System”;    -   U.S. Pat. No. 9,098,495, entitled “Application-Aware and Remote        Single Instance Data Management”;    -   U.S. Pat. No. 9,239,687, entitled “Systems and Methods for        Retaining and Using Data Block Signatures in Data Protection        Operations”;    -   U.S. Patent Application Pub. No. 2006/0224846, entitled “System        and Method to Support Single Instance Storage Operations”;    -   U.S. Patent Application Pub. No. 2014/0201170, entitled “High        Availability Distributed Deduplicated Storage System”;    -   U.S. Patent Application Pub. No. 2016/0350391, entitled        “Replication Using Deduplicated Secondary Copy Data”;    -   U.S. Patent Application Pub. No. 2017/0168903 entitled “Live        Synchronization and Management of Virtual Machines across        Computing and Virtualization Platforms and Using Live        Synchronization to Support Disaster Recovery”;    -   U.S. Patent Application Pub. No. 2017/0193003 entitled        “Redundant and Robust Distributed Deduplication Data Storage        System”;    -   U.S. Patent Application Pub. No. 2017/0235647 entitled “Data        Protection Operations Based on Network Path Information”;    -   U.S. Patent Application Pub. No. 2017/0242871, entitled “Data        Restoration Operations Based on Network Path Information”; and    -   U.S. Patent Application Pub. No. 2017/0185488, entitled        “Application-Level Live Synchronization Across Computing        Platforms Including Synchronizing Co-Resident Applications To        Disparate Standby Destinations And Selectively Synchronizing        Some Applications And Not Others”.

System 100 includes computing devices and computing technologies. Forinstance, system 100 can include one or more client computing devices102 and secondary storage computing devices 106, as well as storagemanager 140 or a host computing device for it. Computing devices caninclude, without limitation, one or more: workstations, personalcomputers, desktop computers, or other types of generally fixedcomputing systems such as mainframe computers, servers, andminicomputers. Other computing devices can include mobile or portablecomputing devices, such as one or more laptops, tablet computers,personal data assistants, mobile phones (such as smartphones), and othermobile or portable computing devices such as embedded computers, set topboxes, vehicle-mounted devices, wearable computers, etc. Servers caninclude mail servers, file servers, database servers, virtual machineservers, and web servers. Any given computing device comprises one ormore processors (e.g., CPU and/or single-core or multi-core processors),as well as corresponding non-transitory computer memory (e.g.,random-access memory (RAM)) for storing computer programs which are tobe executed by the one or more processors. Other computer memory formass storage of data may be packaged/configured with the computingdevice (e.g., an internal hard disk) and/or may be external andaccessible by the computing device (e.g., network-attached storage, astorage array, etc.). In some cases, a computing device includes cloudcomputing resources, which may be implemented as virtual machines. Forinstance, one or more virtual machines may be provided to theorganization by a third-party cloud service vendor.

In some embodiments, computing devices can include one or more virtualmachine(s) running on a physical host computing device (or “hostmachine”) operated by the organization. As one example, the organizationmay use one virtual machine as a database server and another virtualmachine as a mail server, both virtual machines operating on the samehost machine. A Virtual machine (“VM”) is a software implementation of acomputer that does not physically exist and is instead instantiated inan operating system of a physical computer (or host machine) to enableapplications to execute within the VM's environment, i.e., a VM emulatesa physical computer. AVM includes an operating system and associatedvirtual resources, such as computer memory and processor(s). Ahypervisor operates between the VM and the hardware of the physical hostmachine and is generally responsible for creating and running the VMs.Hypervisors are also known in the art as virtual machine monitors or avirtual machine managers or “VMMs”, and may be implemented in software,firmware, and/or specialized hardware installed on the host machine.Examples of hypervisors include ESX Server, by VMware, Inc. of PaloAlto, California; Microsoft Virtual Server and Microsoft Windows ServerHyper-V, both by Microsoft Corporation of Redmond, Washington; Sun xVMby Oracle America Inc. of Santa Clara, California; and Xen by CitrixSystems, Santa Clara, California The hypervisor provides resources toeach virtual operating system such as a virtual processor, virtualmemory, a virtual network device, and a virtual disk. Each virtualmachine has one or more associated virtual disks. The hypervisortypically stores the data of virtual disks in files on the file systemof the physical host machine, called virtual machine disk files (“VMDK”in VMware lingo) or virtual hard disk image files (in Microsoft lingo).For example, VMware's ESX Server provides the Virtual Machine FileSystem (VMFS) for the storage of virtual machine disk files. A virtualmachine reads data from and writes data to its virtual disk much the waythat a physical machine reads data from and writes data to a physicaldisk. Examples of techniques for implementing information management ina cloud computing environment are described in U.S. Pat. No. 8,285,681.Examples of techniques for implementing information management in avirtualized computing environment are described in U.S. Pat. No.8,307,177.

Information management system 100 can also include electronic datastorage devices, generally used for mass storage of data, including,e.g., primary storage devices 104 and secondary storage devices 108.Storage devices can generally be of any suitable type including, withoutlimitation, disk drives, storage arrays (e.g., storage-area network(SAN) and/or network-attached storage (NAS) technology), semiconductormemory (e.g., solid state storage devices), network attached storage(NAS) devices, tape libraries, or other magnetic, non-tape storagedevices, optical media storage devices, DNA/RNA-based memory technology,combinations of the same, etc. In some embodiments, storage devices formpart of a distributed file system. In some cases, storage devices areprovided in a cloud storage environment (e.g., a private cloud or oneoperated by a third-party vendor), whether for primary data or secondarycopies or both.

Depending on context, the term “information management system” can referto generally all of the illustrated hardware and software components inFIG. 1C, or the term may refer to only a subset of the illustratedcomponents. For instance, in some cases, system 100 generally refers toa combination of specialized components used to protect, move, manage,manipulate, analyze, and/or process data and metadata generated byclient computing devices 102. However, system 100 in some cases does notinclude the underlying components that generate and/or store primarydata 112, such as the client computing devices 102 themselves, and theprimary storage devices 104. Likewise secondary storage devices 108(e.g., a third-party provided cloud storage environment) may not be partof system 100. As an example, “information management system” or“storage management system” may sometimes refer to one or more of thefollowing components, which will be described in further detail below:storage manager, data agent, and media agent.

One or more client computing devices 102 may be part of system 100, eachclient computing device 102 having an operating system and at least oneapplication 110 and one or more accompanying data agents executingthereon; and associated with one or more primary storage devices 104storing primary data 112. Client computing device(s) 102 and primarystorage devices 104 may generally be referred to in some cases asprimary storage subsystem 117.

Client Computing Devices, Clients, and Subclients

Typically, a variety of sources in an organization produce data to beprotected and managed. As just one example, in a corporate environmentsuch data sources can be employee workstations and company servers suchas a mail server, a web server, a database server, a transaction server,or the like. In system 100, data generation sources include one or moreclient computing devices 102. A computing device that has a data agent142 installed and operating on it is generally referred to as a “clientcomputing device” 102, and may include any type of computing device,without limitation. A client computing device 102 may be associated withone or more users and/or user accounts.

A “client” is a logical component of information management system 100,which may represent a logical grouping of one or more data agentsinstalled on a client computing device 102. Storage manager 140recognizes a client as a component of system 100, and in someembodiments, may automatically create a client component the first timea data agent 142 is installed on a client computing device 102. Becausedata generated by executable component(s) 110 is tracked by theassociated data agent 142 so that it may be properly protected in system100, a client may be said to generate data and to store the generateddata to primary storage, such as primary storage device 104. However,the terms “client” and “client computing device” as used herein do notimply that a client computing device 102 is necessarily configured inthe client/server sense relative to another computing device such as amail server, or that a client computing device 102 cannot be a server inits own right. As just a few examples, a client computing device 102 canbe and/or include mail servers, file servers, database servers, virtualmachine servers, and/or web servers.

Each client computing device 102 may have application(s) 110 executingthereon which generate and manipulate the data that is to be protectedfrom loss and managed in system 100. Applications 110 generallyfacilitate the operations of an organization, and can include, withoutlimitation, mail server applications (e.g., Microsoft Exchange Server),file system applications, mail client applications (e.g., MicrosoftExchange Client), database applications or database management systems(e.g., SQL, Oracle, SAP, Lotus Notes Database), word processingapplications (e.g., Microsoft Word), spreadsheet applications, financialapplications, presentation applications, graphics and/or videoapplications, browser applications, mobile applications, entertainmentapplications, and so on. Each application 110 may be accompanied by anapplication-specific data agent 142, though not all data agents 142 areapplication-specific or associated with only application. A file system,e.g., Microsoft Windows Explorer, may be considered an application 110and may be accompanied by its own data agent 142. Client computingdevices 102 can have at least one operating system (e.g., MicrosoftWindows, Mac OS X, iOS, IBM z/OS, Linux, other Unix-based operatingsystems, etc.) installed thereon, which may support or host one or morefile systems and other applications 110. In some embodiments, a virtualmachine that executes on a host client computing device 102 may beconsidered an application 110 and may be accompanied by a specific dataagent 142 (e.g., virtual server data agent).

Client computing devices 102 and other components in system 100 can beconnected to one another via one or more electronic communicationpathways 114. For example, a first communication pathway 114 maycommunicatively couple client computing device 102 and secondary storagecomputing device 106; a second communication pathway 114 maycommunicatively couple storage manager 140 and client computing device102; and a third communication pathway 114 may communicatively couplestorage manager 140 and secondary storage computing device 106, etc.(see, e.g., FIG. 1A and FIG. 1C). A communication pathway 114 caninclude one or more networks or other connection types including one ormore of the following, without limitation: the Internet, a wide areanetwork (WAN), a local area network (LAN), a Storage Area Network (SAN),a Fibre Channel (FC) connection, a Small Computer System Interface(SCSI) connection, a virtual private network (VPN), a token ring orTCP/IP based network, an intranet network, a point-to-point link, acellular network, a wireless data transmission system, a two-way cablesystem, an interactive kiosk network, a satellite network, a broadbandnetwork, a baseband network, a neural network, a mesh network, an ad hocnetwork, other appropriate computer or telecommunications networks,combinations of the same or the like. Communication pathways 114 in somecases may also include application programming interfaces (APIs)including, e.g., cloud service provider APIs, virtual machine managementAPIs, and hosted service provider APIs. The underlying infrastructure ofcommunication pathways 114 may be wired and/or wireless, analog and/ordigital, or any combination thereof; and the facilities used may beprivate, public, third-party provided, or any combination thereof,without limitation.

A “subclient” is a logical grouping of all or part of a client's primarydata 112. In general, a subclient may be defined according to how thesubclient data is to be protected as a unit in system 100. For example,a subclient may be associated with a certain storage policy. A givenclient may thus comprise several subclients, each subclient associatedwith a different storage policy. For example, some files may form afirst subclient that requires compression and deduplication and isassociated with a first storage policy. Other files of the client mayform a second subclient that requires a different retention schedule aswell as encryption, and may be associated with a different, secondstorage policy. As a result, though the primary data may be generated bythe same application 110 and may belong to one given client, portions ofthe data may be assigned to different subclients for distinct treatmentby system 100. More detail on subclients is given in regard to storagepolicies below.

Primary Data and Exemplary Primary Storage Devices

Primary data 112 is generally production data or “live” data generatedby the operating system and/or applications 110 executing on clientcomputing device 102. Primary data 112 is generally stored on primarystorage device(s) 104 and is organized via a file system operating onthe client computing device 102. Thus, client computing device(s) 102and corresponding applications 110 may create, access, modify, write,delete, and otherwise use primary data 112. Primary data 112 isgenerally in the native format of the source application 110. Primarydata 112 is an initial or first stored body of data generated by thesource application 110. Primary data 112 in some cases is createdsubstantially directly from data generated by the corresponding sourceapplication 110. It can be useful in performing certain tasks toorganize primary data 112 into units of different granularities. Ingeneral, primary data 112 can include files, directories, file systemvolumes, data blocks, extents, or any other hierarchies or organizationsof data objects. As used herein, a “data object” can refer to (i) anyfile that is currently addressable by a file system or that waspreviously addressable by the file system (e.g., an archive file),and/or to (ii) a subset of such a file (e.g., a data block, an extent,etc.). Primary data 112 may include structured data (e.g., databasefiles), unstructured data (e.g., documents), and/or semi-structureddata. See, e.g., FIG. 1B.

It can also be useful in performing certain functions of system 100 toaccess and modify metadata within primary data 112. Metadata generallyincludes information about data objects and/or characteristicsassociated with the data objects. For simplicity herein, it is to beunderstood that, unless expressly stated otherwise, any reference toprimary data 112 generally also includes its associated metadata, butreferences to metadata generally do not include the primary data.Metadata can include, without limitation, one or more of the following:the data owner (e.g., the client or user that generates the data), thelast modified time (e.g., the time of the most recent modification ofthe data object), a data object name (e.g., a file name), a data objectsize (e.g., a number of bytes of data), information about the content(e.g., an indication as to the existence of a particular search term),user-supplied tags, to/from information for email (e.g., an emailsender, recipient, etc.), creation date, file type (e.g., format orapplication type), last accessed time, application type (e.g., type ofapplication that generated the data object), location/network (e.g., acurrent, past or future location of the data object and network pathwaysto/from the data object), geographic location (e.g., GPS coordinates),frequency of change (e.g., a period in which the data object ismodified), business unit (e.g., a group or department that generates,manages or is otherwise associated with the data object), aginginformation (e.g., a schedule, such as a time period, in which the dataobject is migrated to secondary or long term storage), boot sectors,partition layouts, file location within a file folder directorystructure, user permissions, owners, groups, access control lists(ACLs), system metadata (e.g., registry information), combinations ofthe same or other similar information related to the data object. Inaddition to metadata generated by or related to file systems andoperating systems, some applications 110 and/or other components ofsystem 100 maintain indices of metadata for data objects, e.g., metadataassociated with individual email messages. The use of metadata toperform classification and other functions is described in greaterdetail below.

Primary storage devices 104 storing primary data 112 may be relativelyfast and/or expensive technology (e.g., flash storage, a disk drive, ahard-disk storage array, solid state memory, etc.), typically to supporthigh-performance live production environments. Primary data 112 may behighly changeable and/or may be intended for relatively short termretention (e.g., hours, days, or weeks). According to some embodiments,client computing device 102 can access primary data 112 stored inprimary storage device 104 by making conventional file system calls viathe operating system. Each client computing device 102 is generallyassociated with and/or in communication with one or more primary storagedevices 104 storing corresponding primary data 112. A client computingdevice 102 is said to be associated with or in communication with aparticular primary storage device 104 if it is capable of one or moreof: routing and/or storing data (e.g., primary data 112) to the primarystorage device 104, coordinating the routing and/or storing of data tothe primary storage device 104, retrieving data from the primary storagedevice 104, coordinating the retrieval of data from the primary storagedevice 104, and modifying and/or deleting data in the primary storagedevice 104. Thus, a client computing device 102 may be said to accessdata stored in an associated storage device 104.

Primary storage device 104 may be dedicated or shared. In some cases,each primary storage device 104 is dedicated to an associated clientcomputing device 102, e.g., a local disk drive. In other cases, one ormore primary storage devices 104 can be shared by multiple clientcomputing devices 102, e.g., via a local network, in a cloud storageimplementation, etc. As one example, primary storage device 104 can be astorage array shared by a group of client computing devices 102, such asEMC Clariion, EMC Symmetrix, EMC Celerra, Dell EqualLogic, IBM XIV,NetApp FAS, HP EVA, and HP 3PAR.

System 100 may also include hosted services (not shown), which may behosted in some cases by an entity other than the organization thatemploys the other components of system 100. For instance, the hostedservices may be provided by online service providers. Such serviceproviders can provide social networking services, hosted email services,or hosted productivity applications or other hosted applications such assoftware-as-a-service (SaaS), platform-as-a-service (PaaS), applicationservice providers (ASPs), cloud services, or other mechanisms fordelivering functionality via a network. As it services users, eachhosted service may generate additional data and metadata, which may bemanaged by system 100, e.g., as primary data 112. In some cases, thehosted services may be accessed using one of the applications 110. As anexample, a hosted mail service may be accessed via browser running on aclient computing device 102.

Secondary Copies and Exemplary Secondary Storage Devices

Primary data 112 stored on primary storage devices 104 may becompromised in some cases, such as when an employee deliberately oraccidentally deletes or overwrites primary data 112. Or primary storagedevices 104 can be damaged, lost, or otherwise corrupted. For recoveryand/or regulatory compliance purposes, it is therefore useful togenerate and maintain copies of primary data 112. Accordingly, system100 includes one or more secondary storage computing devices 106 and oneor more secondary storage devices 108 configured to create and store oneor more secondary copies 116 of primary data 112 including itsassociated metadata. The secondary storage computing devices 106 and thesecondary storage devices 108 may be referred to as secondary storagesubsystem 118.

Secondary copies 116 can help in search and analysis efforts and meetother information management goals as well, such as: restoring dataand/or metadata if an original version is lost (e.g., by deletion,corruption, or disaster); allowing point-in-time recovery; complyingwith regulatory data retention and electronic discovery (e-discovery)requirements; reducing utilized storage capacity in the productionsystem and/or in secondary storage; facilitating organization and searchof data; improving user access to data files across multiple computingdevices and/or hosted services; and implementing data retention andpruning policies.

A secondary copy 116 can comprise a separate stored copy of data that isderived from one or more earlier-created stored copies (e.g., derivedfrom primary data 112 or from another secondary copy 116). Secondarycopies 116 can include point-in-time data, and may be intended forrelatively long-term retention before some or all of the data is movedto other storage or discarded. In some cases, a secondary copy 116 maybe in a different storage device than other previously stored copies;and/or may be remote from other previously stored copies. Secondarycopies 116 can be stored in the same storage device as primary data 112.For example, a disk array capable of performing hardware snapshotsstores primary data 112 and creates and stores hardware snapshots of theprimary data 112 as secondary copies 116. Secondary copies 116 may bestored in relatively slow and/or lower cost storage (e.g., magnetictape). A secondary copy 116 may be stored in a backup or archive format,or in some other format different from the native source applicationformat or other format of primary data 112.

Secondary storage computing devices 106 may index secondary copies 116(e.g., using a media agent 144), enabling users to browse and restore ata later time and further enabling the lifecycle management of theindexed data. After creation of a secondary copy 116 that representscertain primary data 112, a pointer or other location indicia (e.g., astub) may be placed in primary data 112, or be otherwise associated withprimary data 112, to indicate the current location of a particularsecondary copy 116. Since an instance of a data object or metadata inprimary data 112 may change over time as it is modified by application110 (or hosted service or the operating system), system 100 may createand manage multiple secondary copies 116 of a particular data object ormetadata, each copy representing the state of the data object in primarydata 112 at a particular point in time. Moreover, since an instance of adata object in primary data 112 may eventually be deleted from primarystorage device 104 and the file system, system 100 may continue tomanage point-in-time representations of that data object, even thoughthe instance in primary data 112 no longer exists. For virtual machines,the operating system and other applications 110 of client computingdevice(s) 102 may execute within or under the management ofvirtualization software (e.g., a VMM), and the primary storage device(s)104 may comprise a virtual disk created on a physical storage device.System 100 may create secondary copies 116 of the files or other dataobjects in a virtual disk file and/or secondary copies 116 of the entirevirtual disk file itself (e.g., of an entire .vmdk file).

Secondary copies 116 are distinguishable from corresponding primary data112. First, secondary copies 116 can be stored in a different formatfrom primary data 112 (e.g., backup, archive, or other non-nativeformat). For this or other reasons, secondary copies 116 may not bedirectly usable by applications 110 or client computing device 102(e.g., via standard system calls or otherwise) without modification,processing, or other intervention by system 100 which may be referred toas “restore” operations. Secondary copies 116 may have been processed bydata agent 142 and/or media agent 144 in the course of being created(e.g., compression, deduplication, encryption, integrity markers,indexing, formatting, application-aware metadata, etc.), and thussecondary copy 116 may represent source primary data 112 withoutnecessarily being exactly identical to the source.

Second, secondary copies 116 may be stored on a secondary storage device108 that is inaccessible to application 110 running on client computingdevice 102 and/or hosted service. Some secondary copies 116 may be“offline copies,” in that they are not readily available (e.g., notmounted to tape or disk). Offline copies can include copies of data thatsystem 100 can access without human intervention (e.g., tapes within anautomated tape library, but not yet mounted in a drive), and copies thatthe system 100 can access only with some human intervention (e.g., tapeslocated at an offsite storage site).

Using Intermediate Devices for Creating Secondary Copies—SecondaryStorage Computing Devices

Creating secondary copies can be challenging when hundreds or thousandsof client computing devices 102 continually generate large volumes ofprimary data 112 to be protected. Also, there can be significantoverhead involved in the creation of secondary copies 116. Moreover,specialized programmed intelligence and/or hardware capability isgenerally needed for accessing and interacting with secondary storagedevices 108. Client computing devices 102 may interact directly with asecondary storage device 108 to create secondary copies 116, but in viewof the factors described above, this approach can negatively impact theability of client computing device 102 to serve/service application 110and produce primary data 112. Further, any given client computing device102 may not be optimized for interaction with certain secondary storagedevices 108.

Thus, system 100 may include one or more software and/or hardwarecomponents which generally act as intermediaries between clientcomputing devices 102 (that generate primary data 112) and secondarystorage devices 108 (that store secondary copies 116). In addition tooff-loading certain responsibilities from client computing devices 102,these intermediate components provide other benefits. For instance, asdiscussed further below with respect to FIG. 1D, distributing some ofthe work involved in creating secondary copies 116 can enhancescalability and improve system performance. For instance, usingspecialized secondary storage computing devices 106 and media agents 144for interfacing with secondary storage devices 108 and/or for performingcertain data processing operations can greatly improve the speed withwhich system 100 performs information management operations and can alsoimprove the capacity of the system to handle large numbers of suchoperations, while reducing the computational load on the productionenvironment of client computing devices 102. The intermediate componentscan include one or more secondary storage computing devices 106 as shownin FIG. 1A and/or one or more media agents 144. Media agents arediscussed further below (e.g., with respect to FIGS. 1C-1E). Thesespecial-purpose components of system 100 comprise specialized programmedintelligence and/or hardware capability for writing to, reading from,instructing, communicating with, or otherwise interacting with secondarystorage devices 108.

Secondary storage computing device(s) 106 can comprise any of thecomputing devices described above, without limitation. In some cases,secondary storage computing device(s) 106 also include specializedhardware componentry and/or software intelligence (e.g., specializedinterfaces) for interacting with certain secondary storage device(s) 108with which they may be specially associated.

To create a secondary copy 116 involving the copying of data fromprimary storage subsystem 117 to secondary storage subsystem 118, clientcomputing device 102 may communicate the primary data 112 to be copied(or a processed version thereof generated by a data agent 142) to thedesignated secondary storage computing device 106, via a communicationpathway 114. Secondary storage computing device 106 in turn may furtherprocess and convey the data or a processed version thereof to secondarystorage device 108. One or more secondary copies 116 may be created fromexisting secondary copies 116, such as in the case of an auxiliary copyoperation, described further below.

Exemplary Primary Data and an Exemplary Secondary Copy

FIG. 1B is a detailed view of some specific examples of primary datastored on primary storage device(s) 104 and secondary copy data storedon secondary storage device(s) 108, with other components of the systemremoved for the purposes of illustration. Stored on primary storagedevice(s) 104 are primary data 112 objects including word processingdocuments 119A-B, spreadsheets 120, presentation documents 122, videofiles 124, image files 126, email mailboxes 128 (and corresponding emailmessages 129A-C), HTML/XML or other types of markup language files 130,databases 132 and corresponding tables or other data structures133A-133C. Some or all primary data 112 objects are associated withcorresponding metadata (e.g., “Meta1-11”), which may include file systemmetadata and/or application-specific metadata. Stored on the secondarystorage device(s) 108 are secondary copy 116 data objects 134A-C whichmay include copies of or may otherwise represent corresponding primarydata 112.

Secondary copy data objects 134A-C can individually represent more thanone primary data object. For example, secondary copy data object 134Arepresents three separate primary data objects 133C, 122, and 129C(represented as 133C′, 122′, and 129C′, respectively, and accompanied bycorresponding metadata Meta11, Meta3, and Meta8, respectively).Moreover, as indicated by the prime mark (′), secondary storagecomputing devices 106 or other components in secondary storage subsystem118 may process the data received from primary storage subsystem 117 andstore a secondary copy including a transformed and/or supplementedrepresentation of a primary data object and/or metadata that isdifferent from the original format, e.g., in a compressed, encrypted,deduplicated, or other modified format. For instance, secondary storagecomputing devices 106 can generate new metadata or other informationbased on said processing, and store the newly generated informationalong with the secondary copies. Secondary copy data object 1346represents primary data objects 120, 1336, and 119A as 120′, 133B′, and119A′, respectively, accompanied by corresponding metadata Meta2,Meta10, and Meta1, respectively. Also, secondary copy data object 134Crepresents primary data objects 133A, 119B, and 129A as 133A′, 119B′,and 129A′, respectively, accompanied by corresponding metadata Meta9,Meta5, and Meta6, respectively.

Exemplary Information Management System Architecture

System 100 can incorporate a variety of different hardware and softwarecomponents, which can in turn be organized with respect to one anotherin many different configurations, depending on the embodiment. There arecritical design choices involved in specifying the functionalresponsibilities of the components and the role of each component insystem 100. Such design choices can impact how system 100 performs andadapts to data growth and other changing circumstances. FIG. 1C shows asystem 100 designed according to these considerations and includes:storage manager 140, one or more data agents 142 executing on clientcomputing device(s) 102 and configured to process primary data 112, andone or more media agents 144 executing on one or more secondary storagecomputing devices 106 for performing tasks involving secondary storagedevices 108.

Storage Manager

Storage manager 140 is a centralized storage and/or information managerthat is configured to perform certain control functions and also tostore certain critical information about system 100—hence storagemanager 140 is said to manage system 100. As noted, the number ofcomponents in system 100 and the amount of data under management can belarge. Managing the components and data is therefore a significant task,which can grow unpredictably as the number of components and data scaleto meet the needs of the organization. For these and other reasons,according to certain embodiments, responsibility for controlling system100, or at least a significant portion of that responsibility, isallocated to storage manager 140. Storage manager 140 can be adaptedindependently according to changing circumstances, without having toreplace or re-design the remainder of the system. Moreover, a computingdevice for hosting and/or operating as storage manager 140 can beselected to best suit the functions and networking needs of storagemanager 140. These and other advantages are described in further detailbelow and with respect to FIG. 1D.

Storage manager 140 may be a software module or other application hostedby a suitable computing device. In some embodiments, storage manager 140is itself a computing device that performs the functions describedherein. Storage manager 140 comprises or operates in conjunction withone or more associated data structures such as a dedicated database(e.g., management database 146), depending on the configuration. Thestorage manager 140 generally initiates, performs, coordinates, and/orcontrols storage and other information management operations performedby system 100, e.g., to protect and control primary data 112 andsecondary copies 116. In general, storage manager 140 is said to managesystem 100, which includes communicating with, instructing, andcontrolling in some circumstances components such as data agents 142 andmedia agents 144, etc.

As shown by the dashed arrowed lines 114 in FIG. 1C, storage manager 140may communicate with, instruct, and/or control some or all elements ofsystem 100, such as data agents 142 and media agents 144. In thismanner, storage manager 140 manages the operation of various hardwareand software components in system 100. In certain embodiments, controlinformation originates from storage manager 140 and status as well asindex reporting is transmitted to storage manager 140 by the managedcomponents, whereas payload data and metadata are generally communicatedbetween data agents 142 and media agents 144 (or otherwise betweenclient computing device(s) 102 and secondary storage computing device(s)106), e.g., at the direction of and under the management of storagemanager 140. Control information can generally include parameters andinstructions for carrying out information management operations, suchas, without limitation, instructions to perform a task associated withan operation, timing information specifying when to initiate a task,data path information specifying what components to communicate with oraccess in carrying out an operation, and the like. In other embodiments,some information management operations are controlled or initiated byother components of system 100 (e.g., by media agents 144 or data agents142), instead of or in combination with storage manager 140.

According to certain embodiments, storage manager 140 provides one ormore of the following functions:

-   -   communicating with data agents 142 and media agents 144,        including transmitting instructions, messages, and/or queries,        as well as receiving status reports, index information,        messages, and/or queries, and responding to same;    -   initiating execution of information management operations;    -   initiating restore and recovery operations;    -   managing secondary storage devices 108 and inventory/capacity of        the same;    -   allocating secondary storage devices 108 for secondary copy        operations;    -   reporting, searching, and/or classification of data in system        100;    -   monitoring completion of and status reporting related to        information management operations and jobs;    -   tracking movement of data within system 100;    -   tracking age information relating to secondary copies 116,        secondary storage devices 108, comparing the age information        against retention guidelines, and initiating data pruning when        appropriate;    -   tracking logical associations between components in system 100;    -   protecting metadata associated with system 100, e.g., in        management database 146;    -   implementing job management, schedule management, event        management, alert management, reporting, job history        maintenance, user security management, disaster recovery        management, and/or user interfacing for system administrators        and/or end users of system 100;    -   sending, searching, and/or viewing of log files; and    -   implementing operations management functionality.

Storage manager 140 may maintain an associated database 146 (or “storagemanager database 146” or “management database 146”) ofmanagement-related data and information management policies 148.Database 146 is stored in computer memory accessible by storage manager140. Database may include a management index 150 (or “index 150”) orother data structure(s) that may store: logical associations betweencomponents of the system; user preferences and/or profiles (e.g.,preferences regarding encryption, compression, or deduplication ofprimary data or secondary copies; preferences regarding the scheduling,type, or other aspects of secondary copy or other operations; mappingsof particular information management users or user accounts to certaincomputing devices or other components, etc.; management tasks; mediacontainerization; other useful data; and/or any combination thereof. Forexample, storage manager 140 may use index 150 to track logicalassociations between media agents 144 and secondary storage devices 108and/or movement of data to/from secondary storage devices 108. Forinstance, index 150 may store data associating a client computing device102 with a particular media agent 144 and/or secondary storage device108, as specified in an information management policy 148.

Administrators and others may configure and initiate certain informationmanagement operations on an individual basis. But while this may beacceptable for some recovery operations or other infrequent tasks, it isoften not workable for implementing on-going organization-wide dataprotection and management. Thus, system 100 may utilize informationmanagement policies 148 for specifying and executing informationmanagement operations on an automated basis. Generally, an informationmanagement policy 148 can include a stored data structure or otherinformation source that specifies parameters (e.g., criteria and rules)associated with storage management or other information managementoperations. Storage manager 140 can process an information managementpolicy 148 and/or index 150 and, based on the results, identify aninformation management operation to perform, identify the appropriatecomponents in system 100 to be involved in the operation (e.g., clientcomputing devices 102 and corresponding data agents 142, secondarystorage computing devices 106 and corresponding media agents 144, etc.),establish connections to those components and/or between thosecomponents, and/or instruct and control those components to carry outthe operation. In this manner, system 100 can translate storedinformation into coordinated activity among the various computingdevices in system 100.

Management database 146 may maintain information management policies 148and associated data, although information management policies 148 can bestored in computer memory at any appropriate location outside managementdatabase 146. For instance, an information management policy 148 such asa storage policy may be stored as metadata in a media agent database 152or in a secondary storage device 108 (e.g., as an archive copy) for usein restore or other information management operations, depending on theembodiment. Information management policies 148 are described furtherbelow. According to certain embodiments, management database 146comprises a relational database (e.g., an SQL database) for trackingmetadata, such as metadata associated with secondary copy operations(e.g., what client computing devices 102 and corresponding subclientdata were protected and where the secondary copies are stored and whichmedia agent 144 performed the storage operation(s)). This and othermetadata may additionally be stored in other locations, such as atsecondary storage computing device 106 or on the secondary storagedevice 108, allowing data recovery without the use of storage manager140 in some cases. Thus, management database 146 may comprise dataneeded to kick off secondary copy operations (e.g., storage policies,schedule policies, etc.), status and reporting information aboutcompleted jobs (e.g., status and error reports on yesterday's backupjobs), and additional information sufficient to enable restore anddisaster recovery operations (e.g., media agent associations, locationindexing, content indexing, etc.).

Storage manager 140 may include a jobs agent 156, a user interface 158,and a management agent 154, all of which may be implemented asinterconnected software modules or application programs. These aredescribed further below.

Jobs agent 156 in some embodiments initiates, controls, and/or monitorsthe status of some or all information management operations previouslyperformed, currently being performed, or scheduled to be performed bysystem 100. A job is a logical grouping of information managementoperations such as daily storage operations scheduled for a certain setof subclients (e.g., generating incremental block-level backup copies116 at a certain time every day for database files in a certaingeographical location). Thus, jobs agent 156 may access informationmanagement policies 148 (e.g., in management database 146) to determinewhen, where, and how to initiate/control jobs in system 100.

Storage Manager User Interfaces

User interface 158 may include information processing and displaysoftware, such as a graphical user interface (GUI), an applicationprogram interface (API), and/or other interactive interface(s) throughwhich users and system processes can retrieve information about thestatus of information management operations or issue instructions tostorage manager 140 and other components. Via user interface 158, usersmay issue instructions to the components in system 100 regardingperformance of secondary copy and recovery operations. For example, auser may modify a schedule concerning the number of pending secondarycopy operations. As another example, a user may employ the GUI to viewthe status of pending secondary copy jobs or to monitor the status ofcertain components in system 100 (e.g., the amount of capacity left in astorage device). Storage manager 140 may track information that permitsit to select, designate, or otherwise identify content indices,deduplication databases, or similar databases or resources or data setswithin its information management cell (or another cell) to be searchedin response to certain queries. Such queries may be entered by the userby interacting with user interface 158.

Various embodiments of information management system 100 may beconfigured and/or designed to generate user interface data usable forrendering the various interactive user interfaces described. The userinterface data may be used by system 100 and/or by another system,device, and/or software program (for example, a browser program), torender the interactive user interfaces. The interactive user interfacesmay be displayed on, for example, electronic displays (including, forexample, touch-enabled displays), consoles, etc., whetherdirect-connected to storage manager 140 or communicatively coupledremotely, e.g., via an internet connection. The present disclosuredescribes various embodiments of interactive and dynamic userinterfaces, some of which may be generated by user interface agent 158,and which are the result of significant technological development. Theuser interfaces described herein may provide improved human-computerinteractions, allowing for significant cognitive and ergonomicefficiencies and advantages over previous systems, including reducedmental workloads, improved decision-making, and the like. User interface158 may operate in a single integrated view or console (not shown). Theconsole may support a reporting capability for generating a variety ofreports, which may be tailored to a particular aspect of informationmanagement.

User interfaces are not exclusive to storage manager 140 and in someembodiments a user may access information locally from a computingdevice component of system 100. For example, some information pertainingto installed data agents 142 and associated data streams may beavailable from client computing device 102. Likewise, some informationpertaining to media agents 144 and associated data streams may beavailable from secondary storage computing device 106.

Storage Manager Management Agent

Management agent 154 can provide storage manager 140 with the ability tocommunicate with other components within system 100 and/or with otherinformation management cells via network protocols and applicationprogramming interfaces (APIs) including, e.g., HTTP, HTTPS, FTP, REST,virtualization software APIs, cloud service provider APIs, and hostedservice provider APIs, without limitation. Management agent 154 alsoallows multiple information management cells to communicate with oneanother. For example, system 100 in some cases may be one informationmanagement cell in a network of multiple cells adjacent to one anotheror otherwise logically related, e.g., in a WAN or LAN. With thisarrangement, the cells may communicate with one another throughrespective management agents 154. Inter-cell communications andhierarchy is described in greater detail in e.g., U.S. Pat. No.7,343,453.

Information Management Cell

An “information management cell” (or “storage operation cell” or “cell”)may generally include a logical and/or physical grouping of acombination of hardware and software components associated withperforming information management operations on electronic data,typically one storage manager 140 and at least one data agent 142(executing on a client computing device 102) and at least one mediaagent 144 (executing on a secondary storage computing device 106). Forinstance, the components shown in FIG. 1C may together form aninformation management cell. Thus, in some configurations, a system 100may be referred to as an information management cell or a storageoperation cell. A given cell may be identified by the identity of itsstorage manager 140, which is generally responsible for managing thecell.

Multiple cells may be organized hierarchically, so that cells mayinherit properties from hierarchically superior cells or be controlledby other cells in the hierarchy (automatically or otherwise).Alternatively, in some embodiments, cells may inherit or otherwise beassociated with information management policies, preferences,information management operational parameters, or other properties orcharacteristics according to their relative position in a hierarchy ofcells. Cells may also be organized hierarchically according to function,geography, architectural considerations, or other factors useful ordesirable in performing information management operations. For example,a first cell may represent a geographic segment of an enterprise, suchas a Chicago office, and a second cell may represent a differentgeographic segment, such as a New York City office. Other cells mayrepresent departments within a particular office, e.g., human resources,finance, engineering, etc. Where delineated by function, a first cellmay perform one or more first types of information management operations(e.g., one or more first types of secondary copies at a certainfrequency), and a second cell may perform one or more second types ofinformation management operations (e.g., one or more second types ofsecondary copies at a different frequency and under different retentionrules). In general, the hierarchical information is maintained by one ormore storage managers 140 that manage the respective cells (e.g., incorresponding management database(s) 146).

Data Agents

A variety of different applications 110 can operate on a given clientcomputing device 102, including operating systems, file systems,database applications, e-mail applications, and virtual machines, justto name a few. And, as part of the process of creating and restoringsecondary copies 116, the client computing device 102 may be tasked withprocessing and preparing the primary data 112 generated by these variousapplications 110. Moreover, the nature of the processing/preparation candiffer across application types, e.g., due to inherent structural,state, and formatting differences among applications 110 and/or theoperating system of client computing device 102. Each data agent 142 istherefore advantageously configured in some embodiments to assist in theperformance of information management operations based on the type ofdata that is being protected at a client-specific and/orapplication-specific level.

Data agent 142 is a component of information system 100 and is generallydirected by storage manager 140 to participate in creating or restoringsecondary copies 116. Data agent 142 may be a software program (e.g., inthe form of a set of executable binary files) that executes on the sameclient computing device 102 as the associated application 110 that dataagent 142 is configured to protect. Data agent 142 is generallyresponsible for managing, initiating, or otherwise assisting in theperformance of information management operations in reference to itsassociated application(s) 110 and corresponding primary data 112 whichis generated/accessed by the particular application(s) 110. Forinstance, data agent 142 may take part in copying, archiving, migrating,and/or replicating of certain primary data 112 stored in the primarystorage device(s) 104. Data agent 142 may receive control informationfrom storage manager 140, such as commands to transfer copies of dataobjects and/or metadata to one or more media agents 144. Data agent 142also may compress, deduplicate, and encrypt certain primary data 112, aswell as capture application-related metadata before transmitting theprocessed data to media agent 144. Data agent 142 also may receiveinstructions from storage manager 140 to restore (or assist inrestoring) a secondary copy 116 from secondary storage device 108 toprimary storage 104, such that the restored data may be properlyaccessed by application 110 in a suitable format as though it wereprimary data 112.

Each data agent 142 may be specialized for a particular application 110.For instance, different individual data agents 142 may be designed tohandle Microsoft Exchange data, Lotus Notes data, Microsoft Windows filesystem data, Microsoft Active Directory Objects data, SQL Server data,SharePoint data, Oracle database data, SAP database data, virtualmachines and/or associated data, and other types of data. A file systemdata agent, for example, may handle data files and/or other file systeminformation. If a client computing device 102 has two or more types ofdata 112, a specialized data agent 142 may be used for each data type.For example, to backup, migrate, and/or restore all of the data on aMicrosoft Exchange server, the client computing device 102 may use: (1)a Microsoft Exchange Mailbox data agent 142 to back up the Exchangemailboxes; (2) a Microsoft Exchange Database data agent 142 to back upthe Exchange databases; (3) a Microsoft Exchange Public Folder dataagent 142 to back up the Exchange Public Folders; and (4) a MicrosoftWindows File System data agent 142 to back up the file system of clientcomputing device 102. In this example, these specialized data agents 142are treated as four separate data agents 142 even though they operate onthe same client computing device 102. Other examples may include archivemanagement data agents such as a migration archiver or a compliancearchiver, Quick Recovery® agents, and continuous data replicationagents. Application-specific data agents 142 can provide improvedperformance as compared to generic agents. For instance, becauseapplication-specific data agents 142 may only handle data for a singlesoftware application, the design, operation, and performance of the dataagent 142 can be streamlined. The data agent 142 may therefore executefaster and consume less persistent storage and/or operating memory thandata agents designed to generically accommodate multiple differentsoftware applications 110.

Each data agent 142 may be configured to access data and/or metadatastored in the primary storage device(s) 104 associated with data agent142 and its host client computing device 102, and process the dataappropriately. For example, during a secondary copy operation, dataagent 142 may arrange or assemble the data and metadata into one or morefiles having a certain format (e.g., a particular backup or archiveformat) before transferring the file(s) to a media agent 144 or othercomponent. The file(s) may include a list of files or other metadata. Insome embodiments, a data agent 142 may be distributed between clientcomputing device 102 and storage manager 140 (and any other intermediatecomponents) or may be deployed from a remote location or its functionsapproximated by a remote process that performs some or all of thefunctions of data agent 142. In addition, a data agent 142 may performsome functions provided by media agent 144. Other embodiments may employone or more generic data agents 142 that can handle and process datafrom two or more different applications 110, or that can handle andprocess multiple data types, instead of or in addition to usingspecialized data agents 142. For example, one generic data agent 142 maybe used to back up, migrate and restore Microsoft Exchange Mailbox dataand Microsoft Exchange Database data, while another generic data agentmay handle Microsoft Exchange Public Folder data and Microsoft WindowsFile System data.

Media Agents

As noted, off-loading certain responsibilities from client computingdevices 102 to intermediate components such as secondary storagecomputing device(s) 106 and corresponding media agent(s) 144 can providea number of benefits including improved performance of client computingdevice 102, faster and more reliable information management operations,and enhanced scalability. In one example which will be discussed furtherbelow, media agent 144 can act as a local cache of recently-copied dataand/or metadata stored to secondary storage device(s) 108, thusimproving restore capabilities and performance for the cached data.

Media agent 144 is a component of system 100 and is generally directedby storage manager 140 in creating and restoring secondary copies 116.Whereas storage manager 140 generally manages system 100 as a whole,media agent 144 provides a portal to certain secondary storage devices108, such as by having specialized features for communicating with andaccessing certain associated secondary storage device 108. Media agent144 may be a software program (e.g., in the form of a set of executablebinary files) that executes on a secondary storage computing device 106.Media agent 144 generally manages, coordinates, and facilitates thetransmission of data between a data agent 142 (executing on clientcomputing device 102) and secondary storage device(s) 108 associatedwith media agent 144. For instance, other components in the system mayinteract with media agent 144 to gain access to data stored onassociated secondary storage device(s) 108, (e.g., to browse, read,write, modify, delete, or restore data). Moreover, media agents 144 cangenerate and store information relating to characteristics of the storeddata and/or metadata, or can generate and store other types ofinformation that generally provides insight into the contents of thesecondary storage devices 108—generally referred to as indexing of thestored secondary copies 116. Each media agent 144 may operate on adedicated secondary storage computing device 106, while in otherembodiments a plurality of media agents 144 may operate on the samesecondary storage computing device 106.

A media agent 144 may be associated with a particular secondary storagedevice 108 if that media agent 144 is capable of one or more of: routingand/or storing data to the particular secondary storage device 108;coordinating the routing and/or storing of data to the particularsecondary storage device 108; retrieving data from the particularsecondary storage device 108; coordinating the retrieval of data fromthe particular secondary storage device 108; and modifying and/ordeleting data retrieved from the particular secondary storage device108. Media agent 144 in certain embodiments is physically separate fromthe associated secondary storage device 108. For instance, a media agent144 may operate on a secondary storage computing device 106 in adistinct housing, package, and/or location from the associated secondarystorage device 108. In one example, a media agent 144 operates on afirst server computer and is in communication with a secondary storagedevice(s) 108 operating in a separate rack-mounted RAID-based system.

A media agent 144 associated with a particular secondary storage device108 may instruct secondary storage device 108 to perform an informationmanagement task. For instance, a media agent 144 may instruct a tapelibrary to use a robotic arm or other retrieval means to load or eject acertain storage media, and to subsequently archive, migrate, or retrievedata to or from that media, e.g., for the purpose of restoring data to aclient computing device 102. As another example, a secondary storagedevice 108 may include an array of hard disk drives or solid statedrives organized in a RAID configuration, and media agent 144 mayforward a logical unit number (LUN) and other appropriate information tothe array, which uses the received information to execute the desiredsecondary copy operation. Media agent 144 may communicate with asecondary storage device 108 via a suitable communications link, such asa SCSI or Fibre Channel link.

Each media agent 144 may maintain an associated media agent database152. Media agent database 152 may be stored to a disk or other storagedevice (not shown) that is local to the secondary storage computingdevice 106 on which media agent 144 executes. In other cases, mediaagent database 152 is stored separately from the host secondary storagecomputing device 106. Media agent database 152 can include, among otherthings, a media agent index 153 (see, e.g., FIG. 1C). In some cases,media agent index 153 does not form a part of and is instead separatefrom media agent database 152.

Media agent index 153 (or “index 153”) may be a data structureassociated with the particular media agent 144 that includes informationabout the stored data associated with the particular media agent andwhich may be generated in the course of performing a secondary copyoperation or a restore. Index 153 provides a fast and efficientmechanism for locating/browsing secondary copies 116 or other datastored in secondary storage devices 108 without having to accesssecondary storage device 108 to retrieve the information from there. Forinstance, for each secondary copy 116, index 153 may include metadatasuch as a list of the data objects (e.g., files/subdirectories, databaseobjects, mailbox objects, etc.), a logical path to the secondary copy116 on the corresponding secondary storage device 108, locationinformation (e.g., offsets) indicating where the data objects are storedin the secondary storage device 108, when the data objects were createdor modified, etc. Thus, index 153 includes metadata associated with thesecondary copies 116 that is readily available for use from media agent144. In some embodiments, some or all of the information in index 153may instead or additionally be stored along with secondary copies 116 insecondary storage device 108. In some embodiments, a secondary storagedevice 108 can include sufficient information to enable a “bare metalrestore,” where the operating system and/or software applications of afailed client computing device 102 or another target may beautomatically restored without manually reinstalling individual softwarepackages (including operating systems).

Because index 153 may operate as a cache, it can also be referred to asan “index cache.” In such cases, information stored in index cache 153typically comprises data that reflects certain particulars aboutrelatively recent secondary copy operations. After some triggeringevent, such as after some time elapses or index cache 153 reaches aparticular size, certain portions of index cache 153 may be copied ormigrated to secondary storage device 108, e.g., on a least-recently-usedbasis. This information may be retrieved and uploaded back into indexcache 153 or otherwise restored to media agent 144 to facilitateretrieval of data from the secondary storage device(s) 108. In someembodiments, the cached information may include format orcontainerization information related to archives or other files storedon storage device(s) 108.

In some alternative embodiments media agent 144 generally acts as acoordinator or facilitator of secondary copy operations between clientcomputing devices 102 and secondary storage devices 108, but does notactually write the data to secondary storage device 108. For instance,storage manager 140 (or media agent 144) may instruct a client computingdevice 102 and secondary storage device 108 to communicate with oneanother directly. In such a case, client computing device 102 transmitsdata directly or via one or more intermediary components to secondarystorage device 108 according to the received instructions, and viceversa. Media agent 144 may still receive, process, and/or maintainmetadata related to the secondary copy operations, i.e., may continue tobuild and maintain index 153. In these embodiments, payload data canflow through media agent 144 for the purposes of populating index 153,but not for writing to secondary storage device 108. Media agent 144and/or other components such as storage manager 140 may in some casesincorporate additional functionality, such as data classification,content indexing, deduplication, encryption, compression, and the like.Further details regarding these and other functions are described below.

Distributed, Scalable Architecture

As described, certain functions of system 100 can be distributed amongstvarious physical and/or logical components. For instance, one or more ofstorage manager 140, data agents 142, and media agents 144 may operateon computing devices that are physically separate from one another. Thisarchitecture can provide a number of benefits. For instance, hardwareand software design choices for each distributed component can betargeted to suit its particular function. The secondary computingdevices 106 on which media agents 144 operate can be tailored forinteraction with associated secondary storage devices 108 and providefast index cache operation, among other specific tasks. Similarly,client computing device(s) 102 can be selected to effectively serviceapplications 110 in order to efficiently produce and store primary data112.

Moreover, in some cases, one or more of the individual components ofinformation management system 100 can be distributed to multipleseparate computing devices. As one example, for large file systems wherethe amount of data stored in management database 146 is relativelylarge, database 146 may be migrated to or may otherwise reside on aspecialized database server (e.g., an SQL server) separate from a serverthat implements the other functions of storage manager 140. Thisdistributed configuration can provide added protection because database146 can be protected with standard database utilities (e.g., SQL logshipping or database replication) independent from other functions ofstorage manager 140. Database 146 can be efficiently replicated to aremote site for use in the event of a disaster or other data loss at theprimary site. Or database 146 can be replicated to another computingdevice within the same site, such as to a higher performance machine inthe event that a storage manager host computing device can no longerservice the needs of a growing system 100.

The distributed architecture also provides scalability and efficientcomponent utilization. FIG. 1D shows an embodiment of informationmanagement system 100 including a plurality of client computing devices102 and associated data agents 142 as well as a plurality of secondarystorage computing devices 106 and associated media agents 144.Additional components can be added or subtracted based on the evolvingneeds of system 100. For instance, depending on where bottlenecks areidentified, administrators can add additional client computing devices102, secondary storage computing devices 106, and/or secondary storagedevices 108. Moreover, where multiple fungible components are available,load balancing can be implemented to dynamically address identifiedbottlenecks. As an example, storage manager 140 may dynamically selectwhich media agents 144 and/or secondary storage devices 108 to use forstorage operations based on a processing load analysis of media agents144 and/or secondary storage devices 108, respectively.

Where system 100 includes multiple media agents 144 (see, e.g., FIG.1D), a first media agent 144 may provide failover functionality for asecond failed media agent 144. In addition, media agents 144 can bedynamically selected to provide load balancing. Each client computingdevice 102 can communicate with, among other components, any of themedia agents 144, e.g., as directed by storage manager 140. And eachmedia agent 144 may communicate with, among other components, any ofsecondary storage devices 108, e.g., as directed by storage manager 140.Thus, operations can be routed to secondary storage devices 108 in adynamic and highly flexible manner, to provide load balancing, failover,etc. Further examples of scalable systems capable of dynamic storageoperations, load balancing, and failover are provided in U.S. Pat. No.7,246,207.

While distributing functionality amongst multiple computing devices canhave certain advantages, in other contexts it can be beneficial toconsolidate functionality on the same computing device. In alternativeconfigurations, certain components may reside and execute on the samecomputing device. As such, in other embodiments, one or more of thecomponents shown in FIG. 1C may be implemented on the same computingdevice. In one configuration, a storage manager 140, one or more dataagents 142, and/or one or more media agents 144 are all implemented onthe same computing device. In other embodiments, one or more data agents142 and one or more media agents 144 are implemented on the samecomputing device, while storage manager 140 is implemented on a separatecomputing device, etc. without limitation.

Exemplary Types of Information Management Operations, Including StorageOperations

In order to protect and leverage stored data, system 100 can beconfigured to perform a variety of information management operations,which may also be referred to in some cases as storage managementoperations or storage operations. These operations can generally include(i) data movement operations, (ii) processing and data manipulationoperations, and (iii) analysis, reporting, and management operations.

Data Movement Operations, Including Secondary Copy Operations

Data movement operations are generally storage operations that involvethe copying or migration of data between different locations in system100. For example, data movement operations can include operations inwhich stored data is copied, migrated, or otherwise transferred from oneor more first storage devices to one or more second storage devices,such as from primary storage device(s) 104 to secondary storagedevice(s) 108, from secondary storage device(s) 108 to differentsecondary storage device(s) 108, from secondary storage devices 108 toprimary storage devices 104, or from primary storage device(s) 104 todifferent primary storage device(s) 104, or in some cases within thesame primary storage device 104 such as within a storage array.

Data movement operations can include by way of example, backupoperations, archive operations, information lifecycle managementoperations such as hierarchical storage management operations,replication operations (e.g., continuous data replication), snapshotoperations, deduplication or single-instancing operations, auxiliarycopy operations, disaster-recovery copy operations, and the like. Aswill be discussed, some of these operations do not necessarily createdistinct copies. Nonetheless, some or all of these operations aregenerally referred to as “secondary copy operations” for simplicity,because they involve secondary copies. Data movement also comprisesrestoring secondary copies.

Backup Operations

A backup operation creates a copy of a version of primary data 112 at aparticular point in time (e.g., one or more files or other data units).Each subsequent backup copy 116 (which is a form of secondary copy 116)may be maintained independently of the first. A backup generallyinvolves maintaining a version of the copied primary data 112 as well asbackup copies 116. Further, a backup copy in some embodiments isgenerally stored in a form that is different from the native format,e.g., a backup format. This contrasts to the version in primary data 112which may instead be stored in a format native to the sourceapplication(s) 110. In various cases, backup copies can be stored in aformat in which the data is compressed, encrypted, deduplicated, and/orotherwise modified from the original native application format. Forexample, a backup copy may be stored in a compressed backup format thatfacilitates efficient long-term storage. Backup copies 116 can haverelatively long retention periods as compared to primary data 112, whichis generally highly changeable. Backup copies 116 may be stored on mediawith slower retrieval times than primary storage device 104. Some backupcopies may have shorter retention periods than some other types ofsecondary copies 116, such as archive copies (described below). Backupsmay be stored at an offsite location.

Backup operations can include full backups, differential backups,incremental backups, “synthetic full” backups, and/or creating a“reference copy.” A full backup (or “standard full backup”) in someembodiments is generally a complete image of the data to be protected.However, because full backup copies can consume a relatively largeamount of storage, it can be useful to use a full backup copy as abaseline and only store changes relative to the full backup copyafterwards.

A differential backup operation (or cumulative incremental backupoperation) tracks and stores changes that occurred since the last fullbackup. Differential backups can grow quickly in size, but can restorerelatively efficiently because a restore can be completed in some casesusing only the full backup copy and the latest differential copy.

An incremental backup operation generally tracks and stores changessince the most recent backup copy of any type, which can greatly reducestorage utilization. In some cases, however, restoring can be lengthycompared to full or differential backups because completing a restoreoperation may involve accessing a full backup in addition to multipleincremental backups.

Synthetic full backups generally consolidate data without directlybacking up data from the client computing device. A synthetic fullbackup is created from the most recent full backup (i.e., standard orsynthetic) and subsequent incremental and/or differential backups. Theresulting synthetic full backup is identical to what would have beencreated had the last backup for the subclient been a standard fullbackup. Unlike standard full, incremental, and differential backups,however, a synthetic full backup does not actually transfer data fromprimary storage to the backup media, because it operates as a backupconsolidator. A synthetic full backup extracts the index data of eachparticipating subclient. Using this index data and the previously backedup user data images, it builds new full backup images (e.g., bitmaps),one for each subclient. The new backup images consolidate the index anduser data stored in the related incremental, differential, and previousfull backups into a synthetic backup file that fully represents thesubclient (e.g., via pointers) but does not comprise all its constituentdata.

Any of the above types of backup operations can be at the volume level,file level, or block level. Volume level backup operations generallyinvolve copying of a data volume (e.g., a logical disk or partition) asa whole. In a file-level backup, information management system 100generally tracks changes to individual files and includes copies offiles in the backup copy. For block-level backups, files are broken intoconstituent blocks, and changes are tracked at the block level. Uponrestore, system 100 reassembles the blocks into files in a transparentfashion. Far less data may actually be transferred and copied tosecondary storage devices 108 during a file-level copy than avolume-level copy. Likewise, a block-level copy may transfer less datathan a file-level copy, resulting in faster execution. However,restoring a relatively higher-granularity copy can result in longerrestore times. For instance, when restoring a block-level copy, theprocess of locating and retrieving constituent blocks can sometimes takelonger than restoring file-level backups.

A reference copy may comprise copy(ies) of selected objects from backedup data, typically to help organize data by keeping contextualinformation from multiple sources together, and/or help retain specificdata for a longer period of time, such as for legal hold needs. Areference copy generally maintains data integrity, and when the data isrestored, it may be viewed in the same format as the source data. Insome embodiments, a reference copy is based on a specialized client,individual subclient and associated information management policies(e.g., storage policy, retention policy, etc.) that are administeredwithin system 100.

Archive Operations

Because backup operations generally involve maintaining a version of thecopied primary data 112 and also maintaining backup copies in secondarystorage device(s) 108, they can consume significant storage capacity. Toreduce storage consumption, an archive operation according to certainembodiments creates an archive copy 116 by both copying and removingsource data. Or, seen another way, archive operations can involve movingsome or all of the source data to the archive destination. Thus, datasatisfying criteria for removal (e.g., data of a threshold age or size)may be removed from source storage. The source data may be primary data112 or a secondary copy 116, depending on the situation. As with backupcopies, archive copies can be stored in a format in which the data iscompressed, encrypted, deduplicated, and/or otherwise modified from theformat of the original application or source copy. In addition, archivecopies may be retained for relatively long periods of time (e.g., years)and, in some cases are never deleted. In certain embodiments, archivecopies may be made and kept for extended periods in order to meetcompliance regulations.

Archiving can also serve the purpose of freeing up space in primarystorage device(s) 104 and easing the demand on computational resourceson client computing device 102. Similarly, when a secondary copy 116 isarchived, the archive copy can therefore serve the purpose of freeing upspace in the source secondary storage device(s) 108. Examples of dataarchiving operations are provided in U.S. Pat. No. 7,107,298.

Snapshot Operations

Snapshot operations can provide a relatively lightweight, efficientmechanism for protecting data. From an end-user viewpoint, a snapshotmay be thought of as an “instant” image of primary data 112 at a givenpoint in time, and may include state and/or status information relativeto an application 110 that creates/manages primary data 112. In oneembodiment, a snapshot may generally capture the directory structure ofan object in primary data 112 such as a file or volume or other data setat a particular moment in time and may also preserve file attributes andcontents. A snapshot in some cases is created relatively quickly, e.g.,substantially instantly, using a minimum amount of file space, but maystill function as a conventional file system backup.

A “hardware snapshot” (or “hardware-based snapshot”) operation occurswhere a target storage device (e.g., a primary storage device 104 or asecondary storage device 108) performs the snapshot operation in aself-contained fashion, substantially independently, using hardware,firmware and/or software operating on the storage device itself. Forinstance, the storage device may perform snapshot operations generallywithout intervention or oversight from any of the other components ofthe system 100, e.g., a storage array may generate an “array-created”hardware snapshot and may also manage its storage, integrity,versioning, etc. In this manner, hardware snapshots can off-load othercomponents of system 100 from snapshot processing. An array may receivea request from another component to take a snapshot and then proceed toexecute the “hardware snapshot” operations autonomously, preferablyreporting success to the requesting component.

A “software snapshot” (or “software-based snapshot”) operation, on theother hand, occurs where a component in system 100 (e.g., clientcomputing device 102, etc.) implements a software layer that manages thesnapshot operation via interaction with the target storage device. Forinstance, the component executing the snapshot management software layermay derive a set of pointers and/or data that represents the snapshot.The snapshot management software layer may then transmit the same to thetarget storage device, along with appropriate instructions for writingthe snapshot. One example of a software snapshot product is MicrosoftVolume Snapshot Service (VSS), which is part of the Microsoft Windowsoperating system.

Some types of snapshots do not actually create another physical copy ofall the data as it existed at the particular point in time, but maysimply create pointers that map files and directories to specific memorylocations (e.g., to specific disk blocks) where the data resides as itexisted at the particular point in time. For example, a snapshot copymay include a set of pointers derived from the file system or from anapplication. In some other cases, the snapshot may be created at theblock-level, such that creation of the snapshot occurs without awarenessof the file system. Each pointer points to a respective stored datablock, so that collectively, the set of pointers reflect the storagelocation and state of the data object (e.g., file(s) or volume(s) ordata set(s)) at the point in time when the snapshot copy was created.

An initial snapshot may use only a small amount of disk space needed torecord a mapping or other data structure representing or otherwisetracking the blocks that correspond to the current state of the filesystem. Additional disk space is usually required only when files anddirectories change later on. Furthermore, when files change, typicallyonly the pointers which map to blocks are copied, not the blocksthemselves. For example for “copy-on-write” snapshots, when a blockchanges in primary storage, the block is copied to secondary storage orcached in primary storage before the block is overwritten in primarystorage, and the pointer to that block is changed to reflect the newlocation of that block. The snapshot mapping of file system data mayalso be updated to reflect the changed block(s) at that particular pointin time. In some other cases, a snapshot includes a full physical copyof all or substantially all of the data represented by the snapshot.Further examples of snapshot operations are provided in U.S. Pat. No.7,529,782. A snapshot copy in many cases can be made quickly and withoutsignificantly impacting primary computing resources because largeamounts of data need not be copied or moved. In some embodiments, asnapshot may exist as a virtual file system, parallel to the actual filesystem. Users in some cases gain read-only access to the record of filesand directories of the snapshot. By electing to restore primary data 112from a snapshot taken at a given point in time, users may also returnthe current file system to the state of the file system that existedwhen the snapshot was taken.

Replication Operations

Replication is another type of secondary copy operation. Some types ofsecondary copies 116 periodically capture images of primary data 112 atparticular points in time (e.g., backups, archives, and snapshots).However, it can also be useful for recovery purposes to protect primarydata 112 in a more continuous fashion, by replicating primary data 112substantially as changes occur. In some cases a replication copy can bea mirror copy, for instance, where changes made to primary data 112 aremirrored or substantially immediately copied to another location (e.g.,to secondary storage device(s) 108). By copying each write operation tothe replication copy, two storage systems are kept synchronized orsubstantially synchronized so that they are virtually identical atapproximately the same time. Where entire disk volumes are mirrored,however, mirroring can require significant amount of storage space andutilizes a large amount of processing resources.

According to some embodiments, secondary copy operations are performedon replicated data that represents a recoverable state, or “known goodstate” of a particular application running on the source system. Forinstance, in certain embodiments, known good replication copies may beviewed as copies of primary data 112. This feature allows the system todirectly access, copy, restore, back up, or otherwise manipulate thereplication copies as if they were the “live” primary data 112. This canreduce access time, storage utilization, and impact on sourceapplications 110, among other benefits. Based on known good stateinformation, system 100 can replicate sections of application data thatrepresent a recoverable state rather than rote copying of blocks ofdata. Examples of replication operations (e.g., continuous datareplication) are provided in U.S. Pat. No. 7,617,262.

Deduplication/Single-Instancing Operations

Deduplication or single-instance storage is useful to reduce the amountof non-primary data. For instance, some or all of the above-describedsecondary copy operations can involve deduplication in some fashion. Newdata is read, broken down into data portions of a selected granularity(e.g., sub-file level blocks, files, etc.), compared with correspondingportions that are already in secondary storage, and only new/changedportions are stored. Portions that already exist are represented aspointers to the already-stored data. Thus, a deduplicated secondary copy116 may comprise actual data portions copied from primary data 112 andmay further comprise pointers to already-stored data, which is generallymore storage-efficient than a full copy.

In order to streamline the comparison process, system 100 may calculateand/or store signatures (e.g., hashes or cryptographically unique IDs)corresponding to the individual source data portions and compare thesignatures to already-stored data signatures, instead of comparingentire data portions. In some cases, only a single instance of each dataportion is stored, and deduplication operations may therefore bereferred to interchangeably as “single-instancing” operations. Dependingon the implementation, however, deduplication operations can store morethan one instance of certain data portions, yet still significantlyreduce stored-data redundancy. Depending on the embodiment,deduplication portions such as data blocks can be of fixed or variablelength. Using variable length blocks can enhance deduplication byresponding to changes in the data stream, but can involve more complexprocessing. In some cases, system 100 utilizes a technique fordynamically aligning deduplication blocks based on changing content inthe data stream, as described in U.S. Pat. No. 8,364,652.

System 100 can deduplicate in a variety of manners at a variety oflocations. For instance, in some embodiments, system 100 implements“target-side” deduplication by deduplicating data at the media agent 144after being received from data agent 142. In some such cases, mediaagents 144 are generally configured to manage the deduplication process.For instance, one or more of the media agents 144 maintain acorresponding deduplication database that stores deduplicationinformation (e.g., datablock signatures). Examples of such aconfiguration are provided in U.S. Pat. No. 9,020,900. Instead of or incombination with “target-side” deduplication, “source-side” (or“client-side”) deduplication can also be performed, e.g., to reduce theamount of data to be transmitted by data agent 142 to media agent 144.Storage manager 140 may communicate with other components within system100 via network protocols and cloud service provider APIs to facilitatecloud-based deduplication/single instancing, as exemplified in U.S. Pat.No. 8,954,446. Some other deduplication/single instancing techniques aredescribed in U.S. Pat. Pub. No. 2006/0224846 and in U.S. Pat. No.9,098,495.

Information Lifecycle Management and Hierarchical Storage Management

In some embodiments, files and other data over their lifetime move frommore expensive quick-access storage to less expensive slower-accessstorage. Operations associated with moving data through various tiers ofstorage are sometimes referred to as information lifecycle management(ILM) operations.

One type of ILM operation is a hierarchical storage management (HSM)operation, which generally automatically moves data between classes ofstorage devices, such as from high-cost to low-cost storage devices. Forinstance, an HSM operation may involve movement of data from primarystorage devices 104 to secondary storage devices 108, or between tiersof secondary storage devices 108. With each tier, the storage devicesmay be progressively cheaper, have relatively slower access/restoretimes, etc. For example, movement of data between tiers may occur asdata becomes less important over time. In some embodiments, an HSMoperation is similar to archiving in that creating an HSM copy may(though not always) involve deleting some of the source data, e.g.,according to one or more criteria related to the source data. Forexample, an HSM copy may include primary data 112 or a secondary copy116 that exceeds a given size threshold or a given age threshold. Often,and unlike some types of archive copies, HSM data that is removed oraged from the source is replaced by a logical reference pointer or stub.The reference pointer or stub can be stored in the primary storagedevice 104 or other source storage device, such as a secondary storagedevice 108 to replace the deleted source data and to point to orotherwise indicate the new location in (another) secondary storagedevice 108.

For example, files are generally moved between higher and lower coststorage depending on how often the files are accessed. When a userrequests access to HSM data that has been removed or migrated, system100 uses the stub to locate the data and may make recovery of the dataappear transparent, even though the HSM data may be stored at a locationdifferent from other source data. In this manner, the data appears tothe user (e.g., in file system browsing windows and the like) as if itstill resides in the source location (e.g., in a primary storage device104). The stub may include metadata associated with the correspondingdata, so that a file system and/or application can provide someinformation about the data object and/or a limited-functionality version(e.g., a preview) of the data object.

An HSM copy may be stored in a format other than the native applicationformat (e.g., compressed, encrypted, deduplicated, and/or otherwisemodified). In some cases, copies which involve the removal of data fromsource storage and the maintenance of stub or other logical referenceinformation on source storage may be referred to generally as “on-linearchive copies.” On the other hand, copies which involve the removal ofdata from source storage without the maintenance of stub or otherlogical reference information on source storage may be referred to as“off-line archive copies.” Examples of HSM and ILM techniques areprovided in U.S. Pat. No. 7,343,453.

Auxiliary Copy Operations

An auxiliary copy is generally a copy of an existing secondary copy 116.For instance, an initial secondary copy 116 may be derived from primarydata 112 or from data residing in secondary storage subsystem 118,whereas an auxiliary copy is generated from the initial secondary copy116. Auxiliary copies provide additional standby copies of data and mayreside on different secondary storage devices 108 than the initialsecondary copies 116. Thus, auxiliary copies can be used for recoverypurposes if initial secondary copies 116 become unavailable. Exemplaryauxiliary copy techniques are described in further detail in U.S. Pat.No. 8,230,195.

Disaster-Recovery Copy Operations

System 100 may also make and retain disaster recovery copies, often assecondary, high-availability disk copies. System 100 may createsecondary copies and store them at disaster recovery locations usingauxiliary copy or replication operations, such as continuous datareplication technologies. Depending on the particular data protectiongoals, disaster recovery locations can be remote from the clientcomputing devices 102 and primary storage devices 104, remote from someor all of the secondary storage devices 108, or both.

Data Manipulation, Including Encryption and Compression

Data manipulation and processing may include encryption and compressionas well as integrity marking and checking, formatting for transmission,formatting for storage, etc. Data may be manipulated “client-side” bydata agent 142 as well as “target-side” by media agent 144 in the courseof creating secondary copy 116, or conversely in the course of restoringdata from secondary to primary.

Encryption Operations

System 100 in some cases is configured to process data (e.g., files orother data objects, primary data 112, secondary copies 116, etc.),according to an appropriate encryption algorithm (e.g., Blowfish,Advanced Encryption Standard (AES), Triple Data Encryption Standard(3-DES), etc.) to limit access and provide data security. System 100 insome cases encrypts the data at the client level, such that clientcomputing devices 102 (e.g., data agents 142) encrypt the data prior totransferring it to other components, e.g., before sending the data tomedia agents 144 during a secondary copy operation. In such cases,client computing device 102 may maintain or have access to an encryptionkey or passphrase for decrypting the data upon restore. Encryption canalso occur when media agent 144 creates auxiliary copies or archivecopies. Encryption may be applied in creating a secondary copy 116 of apreviously unencrypted secondary copy 116, without limitation. Infurther embodiments, secondary storage devices 108 can implementbuilt-in, high performance hardware-based encryption.

Compression Operations

Similar to encryption, system 100 may also or alternatively compressdata in the course of generating a secondary copy 116. Compressionencodes information such that fewer bits are needed to represent theinformation as compared to the original representation. Compressiontechniques are well known in the art. Compression operations may applyone or more data compression algorithms. Compression may be applied increating a secondary copy 116 of a previously uncompressed secondarycopy, e.g., when making archive copies or disaster recovery copies. Theuse of compression may result in metadata that specifies the nature ofthe compression, so that data may be uncompressed on restore ifappropriate.

Data Analysis, Reporting, and Management Operations

Data analysis, reporting, and management operations can differ from datamovement operations in that they do not necessarily involve copying,migration or other transfer of data between different locations in thesystem. For instance, data analysis operations may involve processing(e.g., offline processing) or modification of already stored primarydata 112 and/or secondary copies 116. However, in some embodiments dataanalysis operations are performed in conjunction with data movementoperations. Some data analysis operations include content indexingoperations and classification operations which can be useful inleveraging data under management to enhance search and other features.

Classification Operations/Content Indexing

In some embodiments, information management system 100 analyzes andindexes characteristics, content, and metadata associated with primarydata 112 (“online content indexing”) and/or secondary copies 116(“off-line content indexing”). Content indexing can identify files orother data objects based on content (e.g., user-defined keywords orphrases, other keywords/phrases that are not defined by a user, etc.),and/or metadata (e.g., email metadata such as “to,” “from,” “cc,” “bcc,”attachment name, received time, etc.). Content indexes may be searchedand search results may be restored.

System 100 generally organizes and catalogues the results into a contentindex, which may be stored within media agent database 152, for example.The content index can also include the storage locations of or pointerreferences to indexed data in primary data 112 and/or secondary copies116. Results may also be stored elsewhere in system 100 (e.g., inprimary storage device 104 or in secondary storage device 108). Suchcontent index data provides storage manager 140 or other components withan efficient mechanism for locating primary data 112 and/or secondarycopies 116 of data objects that match particular criteria, thus greatlyincreasing the search speed capability of system 100. For instance,search criteria can be specified by a user through user interface 158 ofstorage manager 140. Moreover, when system 100 analyzes data and/ormetadata in secondary copies 116 to create an “off-line content index,”this operation has no significant impact on the performance of clientcomputing devices 102 and thus does not take a toll on the productionenvironment. Examples of content indexing techniques are provided inU.S. Pat. No. 8,170,995.

One or more components, such as a content index engine, can beconfigured to scan data and/or associated metadata for classificationpurposes to populate a database (or other data structure) ofinformation, which can be referred to as a “data classificationdatabase” or a “metabase.” Depending on the embodiment, the dataclassification database(s) can be organized in a variety of differentways, including centralization, logical sub-divisions, and/or physicalsub-divisions. For instance, one or more data classification databasesmay be associated with different subsystems or tiers within system 100.As an example, there may be a first metabase associated with primarystorage subsystem 117 and a second metabase associated with secondarystorage subsystem 118. In other cases, metabase(s) may be associatedwith individual components, e.g., client computing devices 102 and/ormedia agents 144. In some embodiments, a data classification databasemay reside as one or more data structures within management database146, may be otherwise associated with storage manager 140, and/or mayreside as a separate component. In some cases, metabase(s) may beincluded in separate database(s) and/or on separate storage device(s)from primary data 112 and/or secondary copies 116, such that operationsrelated to the metabase(s) do not significantly impact performance onother components of system 100. In other cases, metabase(s) may bestored along with primary data 112 and/or secondary copies 116. Files orother data objects can be associated with identifiers (e.g., tagentries, etc.) to facilitate searches of stored data objects. Among anumber of other benefits, the metabase can also allow efficient,automatic identification of files or other data objects to associatewith secondary copy or other information management operations. Forinstance, a metabase can dramatically improve the speed with whichsystem 100 can search through and identify data as compared to otherapproaches that involve scanning an entire file system. Examples ofmetabases and data classification operations are provided in U.S. Pat.Nos. 7,734,669 and 7,747,579.

Management and Reporting Operations

Certain embodiments leverage the integrated ubiquitous nature of system100 to provide useful system-wide management and reporting. Operationsmanagement can generally include monitoring and managing the health andperformance of system 100 by, without limitation, performing errortracking, generating granular storage/performance metrics (e.g., jobsuccess/failure information, deduplication efficiency, etc.), generatingstorage modeling and costing information, and the like. As an example,storage manager 140 or another component in system 100 may analyzetraffic patterns and suggest and/or automatically route data to minimizecongestion. In some embodiments, the system can generate predictionsrelating to storage operations or storage operation information. Suchpredictions, which may be based on a trending analysis, may predictvarious network operations or resource usage, such as network trafficlevels, storage media use, use of bandwidth of communication links, useof media agent components, etc. Further examples of traffic analysis,trend analysis, prediction generation, and the like are described inU.S. Pat. No. 7,343,453.

In some configurations having a hierarchy of storage operation cells, amaster storage manager 140 may track the status of subordinate cells,such as the status of jobs, system components, system resources, andother items, by communicating with storage managers 140 (or othercomponents) in the respective storage operation cells. Moreover, themaster storage manager 140 may also track status by receiving periodicstatus updates from the storage managers 140 (or other components) inthe respective cells regarding jobs, system components, systemresources, and other items. In some embodiments, a master storagemanager 140 may store status information and other information regardingits associated storage operation cells and other system information inits management database 146 and/or index 150 (or in another location).The master storage manager 140 or other component may also determinewhether certain storage-related or other criteria are satisfied, and mayperform an action or trigger event (e.g., data migration) in response tothe criteria being satisfied, such as where a storage threshold is metfor a particular volume, or where inadequate protection exists forcertain data. For instance, data from one or more storage operationcells is used to dynamically and automatically mitigate recognizedrisks, and/or to advise users of risks or suggest actions to mitigatethese risks. For example, an information management policy may specifycertain requirements (e.g., that a storage device should maintain acertain amount of free space, that secondary copies should occur at aparticular interval, that data should be aged and migrated to otherstorage after a particular period, that data on a secondary volumeshould always have a certain level of availability and be restorablewithin a given time period, that data on a secondary volume may bemirrored or otherwise migrated to a specified number of other volumes,etc.). If a risk condition or other criterion is triggered, the systemmay notify the user of these conditions and may suggest (orautomatically implement) a mitigation action to address the risk. Forexample, the system may indicate that data from a primary copy 112should be migrated to a secondary storage device 108 to free up space onprimary storage device 104. Examples of the use of risk factors andother triggering criteria are described in U.S. Pat. No. 7,343,453.

In some embodiments, system 100 may also determine whether a metric orother indication satisfies particular storage criteria sufficient toperform an action. For example, a storage policy or other definitionmight indicate that a storage manager 140 should initiate a particularaction if a storage metric or other indication drops below or otherwisefails to satisfy specified criteria such as a threshold of dataprotection. In some embodiments, risk factors may be quantified intocertain measurable service or risk levels. For example, certainapplications and associated data may be considered to be more importantrelative to other data and services. Financial compliance data, forexample, may be of greater importance than marketing materials, etc.Network administrators may assign priority values or “weights” tocertain data and/or applications corresponding to the relativeimportance. The level of compliance of secondary copy operationsspecified for these applications may also be assigned a certain value.Thus, the health, impact, and overall importance of a service may bedetermined, such as by measuring the compliance value and calculatingthe product of the priority value and the compliance value to determinethe “service level” and comparing it to certain operational thresholdsto determine whether it is acceptable. Further examples of the servicelevel determination are provided in U.S. Pat. No. 7,343,453.

System 100 may additionally calculate data costing and data availabilityassociated with information management operation cells. For instance,data received from a cell may be used in conjunction withhardware-related information and other information about system elementsto determine the cost of storage and/or the availability of particulardata. Exemplary information generated could include how fast aparticular department is using up available storage space, how long datawould take to recover over a particular pathway from a particularsecondary storage device, costs over time, etc. Moreover, in someembodiments, such information may be used to determine or predict theoverall cost associated with the storage of certain information. Thecost associated with hosting a certain application may be based, atleast in part, on the type of media on which the data resides, forexample. Storage devices may be assigned to a particular costcategories, for example. Further examples of costing techniques aredescribed in U.S. Pat. No. 7,343,453.

Any of the above types of information (e.g., information related totrending, predictions, job, cell or component status, risk, servicelevel, costing, etc.) can generally be provided to users via userinterface 158 in a single integrated view or console (not shown). Reporttypes may include: scheduling, event management, media management anddata aging. Available reports may also include backup history, dataaging history, auxiliary copy history, job history, library and drive,media in library, restore history, and storage policy, etc., withoutlimitation. Such reports may be specified and created at a certain pointin time as a system analysis, forecasting, or provisioning tool.Integrated reports may also be generated that illustrate storage andperformance metrics, risks and storage costing information. Moreover,users may create their own reports based on specific needs. Userinterface 158 can include an option to graphically depict the variouscomponents in the system using appropriate icons. As one example, userinterface 158 may provide a graphical depiction of primary storagedevices 104, secondary storage devices 108, data agents 142 and/or mediaagents 144, and their relationship to one another in system 100.

In general, the operations management functionality of system 100 canfacilitate planning and decision-making. For example, in someembodiments, a user may view the status of some or all jobs as well asthe status of each component of information management system 100. Usersmay then plan and make decisions based on this data. For instance, auser may view high-level information regarding secondary copy operationsfor system 100, such as job status, component status, resource status(e.g., communication pathways, etc.), and other information. The usermay also drill down or use other means to obtain more detailedinformation regarding a particular component, job, or the like. Furtherexamples are provided in U.S. Pat. No. 7,343,453.

System 100 can also be configured to perform system-wide e-discoveryoperations in some embodiments. In general, e-discovery operationsprovide a unified collection and search capability for data in thesystem, such as data stored in secondary storage devices 108 (e.g.,backups, archives, or other secondary copies 116). For example, system100 may construct and maintain a virtual repository for data stored insystem 100 that is integrated across source applications 110, differentstorage device types, etc. According to some embodiments, e-discoveryutilizes other techniques described herein, such as data classificationand/or content indexing.

Information Management Policies

An information management policy 148 can include a data structure orother information source that specifies a set of parameters (e.g.,criteria and rules) associated with secondary copy and/or otherinformation management operations.

One type of information management policy 148 is a “storage policy.”According to certain embodiments, a storage policy generally comprises adata structure or other information source that defines (or includesinformation sufficient to determine) a set of preferences or othercriteria for performing information management operations. Storagepolicies can include one or more of the following: (1) what data will beassociated with the storage policy, e.g., subclient; (2) a destinationto which the data will be stored; (3) datapath information specifyinghow the data will be communicated to the destination; (4) the type ofsecondary copy operation to be performed; and (5) retention informationspecifying how long the data will be retained at the destination (see,e.g., FIG. 1E). Data associated with a storage policy can be logicallyorganized into subclients, which may represent primary data 112 and/orsecondary copies 116. A subclient may represent static or dynamicassociations of portions of a data volume. Subclients may representmutually exclusive portions. Thus, in certain embodiments, a portion ofdata may be given a label and the association is stored as a staticentity in an index, database or other storage location. Subclients mayalso be used as an effective administrative scheme of organizing dataaccording to data type, department within the enterprise, storagepreferences, or the like. Depending on the configuration, subclients cancorrespond to files, folders, virtual machines, databases, etc. In oneexemplary scenario, an administrator may find it preferable to separatee-mail data from financial data using two different subclients.

A storage policy can define where data is stored by specifying a targetor destination storage device (or group of storage devices). Forinstance, where the secondary storage device 108 includes a group ofdisk libraries, the storage policy may specify a particular disk libraryfor storing the subclients associated with the policy. As anotherexample, where the secondary storage devices 108 include one or moretape libraries, the storage policy may specify a particular tape libraryfor storing the subclients associated with the storage policy, and mayalso specify a drive pool and a tape pool defining a group of tapedrives and a group of tapes, respectively, for use in storing thesubclient data. While information in the storage policy can bestatically assigned in some cases, some or all of the information in thestorage policy can also be dynamically determined based on criteria setforth in the storage policy. For instance, based on such criteria, aparticular destination storage device(s) or other parameter of thestorage policy may be determined based on characteristics associatedwith the data involved in a particular secondary copy operation, deviceavailability (e.g., availability of a secondary storage device 108 or amedia agent 144), network status and conditions (e.g., identifiedbottlenecks), user credentials, and the like.

Datapath information can also be included in the storage policy. Forinstance, the storage policy may specify network pathways and componentsto utilize when moving the data to the destination storage device(s). Insome embodiments, the storage policy specifies one or more media agents144 for conveying data associated with the storage policy between thesource and destination. A storage policy can also specify the type(s) ofassociated operations, such as backup, archive, snapshot, auxiliarycopy, or the like. Furthermore, retention parameters can specify howlong the resulting secondary copies 116 will be kept (e.g., a number ofdays, months, years, etc.), perhaps depending on organizational needsand/or compliance criteria.

When adding a new client computing device 102, administrators canmanually configure information management policies 148 and/or othersettings, e.g., via user interface 158. However, this can be an involvedprocess resulting in delays, and it may be desirable to begin dataprotection operations quickly, without awaiting human intervention.Thus, in some embodiments, system 100 automatically applies a defaultconfiguration to client computing device 102. As one example, when oneor more data agent(s) 142 are installed on a client computing device102, the installation script may register the client computing device102 with storage manager 140, which in turn applies the defaultconfiguration to the new client computing device 102. In this manner,data protection operations can begin substantially immediately. Thedefault configuration can include a default storage policy, for example,and can specify any appropriate information sufficient to begin dataprotection operations. This can include a type of data protectionoperation, scheduling information, a target secondary storage device108, data path information (e.g., a particular media agent 144), and thelike.

Another type of information management policy 148 is a “schedulingpolicy,” which specifies when and how often to perform operations.Scheduling parameters may specify with what frequency (e.g., hourly,weekly, daily, event-based, etc.) or under what triggering conditionssecondary copy or other information management operations are to takeplace. Scheduling policies in some cases are associated with particularcomponents, such as a subclient, client computing device 102, and thelike.

Another type of information management policy 148 is an “audit policy”(or “security policy”), which comprises preferences, rules and/orcriteria that protect sensitive data in system 100. For example, anaudit policy may define “sensitive objects” which are files or dataobjects that contain particular keywords (e.g., “confidential,” or“privileged”) and/or are associated with particular keywords (e.g., inmetadata) or particular flags (e.g., in metadata identifying a documentor email as personal, confidential, etc.). An audit policy may furtherspecify rules for handling sensitive objects. As an example, an auditpolicy may require that a reviewer approve the transfer of any sensitiveobjects to a cloud storage site, and that if approval is denied for aparticular sensitive object, the sensitive object should be transferredto a local primary storage device 104 instead. To facilitate thisapproval, the audit policy may further specify how a secondary storagecomputing device 106 or other system component should notify a reviewerthat a sensitive object is slated for transfer.

Another type of information management policy 148 is a “provisioningpolicy,” which can include preferences, priorities, rules, and/orcriteria that specify how client computing devices 102 (or groupsthereof) may utilize system resources, such as available storage oncloud storage and/or network bandwidth. A provisioning policy specifies,for example, data quotas for particular client computing devices 102(e.g., a number of gigabytes that can be stored monthly, quarterly orannually). Storage manager 140 or other components may enforce theprovisioning policy. For instance, media agents 144 may enforce thepolicy when transferring data to secondary storage devices 108. If aclient computing device 102 exceeds a quota, a budget for the clientcomputing device 102 (or associated department) may be adjustedaccordingly or an alert may trigger.

While the above types of information management policies 148 aredescribed as separate policies, one or more of these can be generallycombined into a single information management policy 148. For instance,a storage policy may also include or otherwise be associated with one ormore scheduling, audit, or provisioning policies or operationalparameters thereof. Moreover, while storage policies are typicallyassociated with moving and storing data, other policies may beassociated with other types of information management operations. Thefollowing is a non-exhaustive list of items that information managementpolicies 148 may specify:

-   -   schedules or other timing information, e.g., specifying when        and/or how often to perform information management operations;    -   the type of secondary copy 116 and/or copy format (e.g.,        snapshot, backup, archive, HSM, etc.);    -   a location or a class or quality of storage for storing        secondary copies 116 (e.g., one or more particular secondary        storage devices 108);    -   preferences regarding whether and how to encrypt, compress,        deduplicate, or otherwise modify or transform secondary copies        116;    -   which system components and/or network pathways (e.g., preferred        media agents 144) should be used to perform secondary storage        operations;    -   resource allocation among different computing devices or other        system components used in performing information management        operations (e.g., bandwidth allocation, available storage        capacity, etc.);    -   whether and how to synchronize or otherwise distribute files or        other data objects across multiple computing devices or hosted        services; and    -   retention information specifying the length of time primary data        112 and/or secondary copies 116 should be retained, e.g., in a        particular class or tier of storage devices, or within the        system 100.

Information management policies 148 can additionally specify or dependon historical or current criteria that may be used to determine whichrules to apply to a particular data object, system component, orinformation management operation, such as:

-   -   frequency with which primary data 112 or a secondary copy 116 of        a data object or metadata has been or is predicted to be used,        accessed, or modified;    -   time-related factors (e.g., aging information such as time since        the creation or modification of a data object);    -   deduplication information (e.g., hashes, data blocks,        deduplication block size, deduplication efficiency or other        metrics);    -   an estimated or historic usage or cost associated with different        components (e.g., with secondary storage devices 108);    -   the identity of users, applications 110, client computing        devices 102 and/or other computing devices that created,        accessed, modified, or otherwise utilized primary data 112 or        secondary copies 116;    -   a relative sensitivity (e.g., confidentiality, importance) of a        data object, e.g., as determined by its content and/or metadata;    -   the current or historical storage capacity of various storage        devices;    -   the current or historical network capacity of network pathways        connecting various components within the storage operation cell;    -   access control lists or other security information; and    -   the content of a particular data object (e.g., its textual        content) or of metadata associated with the data object.

Exemplary Storage Policy and Secondary Copy Operations

FIG. 1E includes a data flow diagram depicting performance of secondarycopy operations by an embodiment of information management system 100,according to an exemplary storage policy 148A. System 100 includes astorage manager 140, a client computing device 102 having a file systemdata agent 142A and an email data agent 142B operating thereon, aprimary storage device 104, two media agents 144A, 144B, and twosecondary storage devices 108: a disk library 108A and a tape library108B. As shown, primary storage device 104 includes primary data 112A,which is associated with a logical grouping of data associated with afile system (“file system subclient”), and primary data 112B, which is alogical grouping of data associated with email (“email subclient”). Thetechniques described with respect to FIG. 1E can be utilized inconjunction with data that is otherwise organized as well.

As indicated by the dashed box, the second media agent 144B and tapelibrary 108B are “off-site,” and may be remotely located from the othercomponents in system 100 (e.g., in a different city, office building,etc.). Indeed, “off-site” may refer to a magnetic tape located in remotestorage, which must be manually retrieved and loaded into a tape driveto be read. In this manner, information stored on the tape library 108Bmay provide protection in the event of a disaster or other failure atthe main site(s) where data is stored.

The file system subclient 112A in certain embodiments generallycomprises information generated by the file system and/or operatingsystem of client computing device 102, and can include, for example,file system data (e.g., regular files, file tables, mount points, etc.),operating system data (e.g., registries, event logs, etc.), and thelike. The e-mail subclient 112B can include data generated by an e-mailapplication operating on client computing device 102, e.g., mailboxinformation, folder information, emails, attachments, associateddatabase information, and the like. As described above, the subclientscan be logical containers, and the data included in the correspondingprimary data 112A and 112B may or may not be stored contiguously.

The exemplary storage policy 148A includes backup copy preferences orrule set 160, disaster recovery copy preferences or rule set 162, andcompliance copy preferences or rule set 164. Backup copy rule set 160specifies that it is associated with file system subclient 166 and emailsubclient 168. Each of subclients 166 and 168 are associated with theparticular client computing device 102. Backup copy rule set 160 furtherspecifies that the backup operation will be written to disk library 108Aand designates a particular media agent 144A to convey the data to disklibrary 108A. Finally, backup copy rule set 160 specifies that backupcopies created according to rule set 160 are scheduled to be generatedhourly and are to be retained for 30 days. In some other embodiments,scheduling information is not included in storage policy 148A and isinstead specified by a separate scheduling policy.

Disaster recovery copy rule set 162 is associated with the same twosubclients 166 and 168. However, disaster recovery copy rule set 162 isassociated with tape library 108B, unlike backup copy rule set 160.Moreover, disaster recovery copy rule set 162 specifies that a differentmedia agent, namely 144B, will convey data to tape library 108B.Disaster recovery copies created according to rule set 162 will beretained for 60 days and will be generated daily. Disaster recoverycopies generated according to disaster recovery copy rule set 162 canprovide protection in the event of a disaster or other catastrophic dataloss that would affect the backup copy 116A maintained on disk library108A.

Compliance copy rule set 164 is only associated with the email subclient168, and not the file system subclient 166. Compliance copies generatedaccording to compliance copy rule set 164 will therefore not includeprimary data 112A from the file system subclient 166. For instance, theorganization may be under an obligation to store and maintain copies ofemail data for a particular period of time (e.g., 10 years) to complywith state or federal regulations, while similar regulations do notapply to file system data. Compliance copy rule set 164 is associatedwith the same tape library 108B and media agent 144B as disasterrecovery copy rule set 162, although a different storage device or mediaagent could be used in other embodiments. Finally, compliance copy ruleset 164 specifies that the copies it governs will be generated quarterlyand retained for 10 years.

Secondary Copy Jobs

A logical grouping of secondary copy operations governed by a rule setand being initiated at a point in time may be referred to as a“secondary copy job” (and sometimes may be called a “backup job,” eventhough it is not necessarily limited to creating only backup copies).Secondary copy jobs may be initiated on demand as well. Steps 1-9 belowillustrate three secondary copy jobs based on storage policy 148A.

Referring to FIG. 1E, at step 1, storage manager 140 initiates a backupjob according to the backup copy rule set 160, which logically comprisesall the secondary copy operations necessary to effectuate rules 160 instorage policy 148A every hour, including steps 1-4 occurring hourly.For instance, a scheduling service running on storage manager 140accesses backup copy rule set 160 or a separate scheduling policyassociated with client computing device 102 and initiates a backup jobon an hourly basis. Thus, at the scheduled time, storage manager 140sends instructions to client computing device 102 (i.e., to both dataagent 142A and data agent 142B) to begin the backup job.

At step 2, file system data agent 142A and email data agent 142B onclient computing device 102 respond to instructions from storage manager140 by accessing and processing the respective subclient primary data112A and 112B involved in the backup copy operation, which can be foundin primary storage device 104. Because the secondary copy operation is abackup copy operation, the data agent(s) 142A, 142B may format the datainto a backup format or otherwise process the data suitable for a backupcopy.

At step 3, client computing device 102 communicates the processed filesystem data (e.g., using file system data agent 142A) and the processedemail data (e.g., using email data agent 142B) to the first media agent144A according to backup copy rule set 160, as directed by storagemanager 140. Storage manager 140 may further keep a record in managementdatabase 146 of the association between media agent 144A and one or moreof: client computing device 102, file system subclient 112A, file systemdata agent 142A, email subclient 112B, email data agent 142B, and/orbackup copy 116A.

The target media agent 144A receives the data-agent-processed data fromclient computing device 102, and at step 4 generates and conveys backupcopy 116A to disk library 108A to be stored as backup copy 116A, againat the direction of storage manager 140 and according to backup copyrule set 160. Media agent 144A can also update its index 153 to includedata and/or metadata related to backup copy 116A, such as informationindicating where the backup copy 116A resides on disk library 108A,where the email copy resides, where the file system copy resides, dataand metadata for cache retrieval, etc. Storage manager 140 may similarlyupdate its index 150 to include information relating to the secondarycopy operation, such as information relating to the type of operation, aphysical location associated with one or more copies created by theoperation, the time the operation was performed, status informationrelating to the operation, the components involved in the operation, andthe like. In some cases, storage manager 140 may update its index 150 toinclude some or all of the information stored in index 153 of mediaagent 144A. At this point, the backup job may be considered complete.After the 30-day retention period expires, storage manager 140 instructsmedia agent 144A to delete backup copy 116A from disk library 108A andindexes 150 and/or 153 are updated accordingly.

At step 5, storage manager 140 initiates another backup job for adisaster recovery copy according to the disaster recovery rule set 162.This includes steps 5-7 occurring daily for creating disaster recoverycopy 116B. By way of illustrating the scalable aspects and off-loadingprinciples embedded in system 100, disaster recovery copy 116B is basedon backup copy 116A and not on primary data 112A and 112B.

At step 6, based on instructions received from storage manager 140 atstep 5, the specified media agent 144B retrieves the most recent backupcopy 116A from disk library 108A.

At step 7, again at the direction of storage manager 140 and asspecified in disaster recovery copy rule set 162, media agent 144B usesthe retrieved data to create a disaster recovery copy 116B and store itto tape library 108B. In some cases, disaster recovery copy 116B is adirect, mirror copy of backup copy 116A, and remains in the backupformat. In other embodiments, disaster recovery copy 116B may be furthercompressed or encrypted, or may be generated in some other manner, suchas by using primary data 112A and 112B from primary storage device 104as sources. The disaster recovery copy operation is initiated once a dayand disaster recovery copies 116B are deleted after 60 days; indexes 153and/or 150 are updated accordingly when/after each informationmanagement operation is executed and/or completed. The present backupjob may be considered completed.

At step 8, storage manager 140 initiates another backup job according tocompliance rule set 164, which performs steps 8-9 quarterly to createcompliance copy 116C. For instance, storage manager 140 instructs mediaagent 144B to create compliance copy 116C on tape library 108B, asspecified in the compliance copy rule set 164.

At step 9 in the example, compliance copy 116C is generated usingdisaster recovery copy 116B as the source. This is efficient, becausedisaster recovery copy resides on the same secondary storage device andthus no network resources are required to move the data. In otherembodiments, compliance copy 116C is instead generated using primarydata 112B corresponding to the email subclient or using backup copy 116Afrom disk library 108A as source data. As specified in the illustratedexample, compliance copies 116C are created quarterly, and are deletedafter ten years, and indexes 153 and/or 150 are kept up-to-dateaccordingly.

Exemplary Applications of Storage Policies—Information GovernancePolicies and Classification

Again referring to FIG. 1E, storage manager 140 may permit a user tospecify aspects of storage policy 148A. For example, the storage policycan be modified to include information governance policies to define howdata should be managed in order to comply with a certain regulation orbusiness objective. The various policies may be stored, for example, inmanagement database 146. An information governance policy may align withone or more compliance tasks that are imposed by regulations or businessrequirements. Examples of information governance policies might includea Sarbanes-Oxley policy, a HIPAA policy, an electronic discovery(e-discovery) policy, and so on.

Information governance policies allow administrators to obtain differentperspectives on an organization's online and offline data, without theneed for a dedicated data silo created solely for each differentviewpoint. As described previously, the data storage systems hereinbuild an index that reflects the contents of a distributed data set thatspans numerous clients and storage devices, including both primary dataand secondary copies, and online and offline copies. An organization mayapply multiple information governance policies in a top-down manner overthat unified data set and indexing schema in order to view andmanipulate the data set through different lenses, each of which isadapted to a particular compliance or business goal. Thus, for example,by applying an e-discovery policy and a Sarbanes-Oxley policy, twodifferent groups of users in an organization can conduct two verydifferent analyses of the same underlying physical set of data/copies,which may be distributed throughout the information management system.

An information governance policy may comprise a classification policy,which defines a taxonomy of classification terms or tags relevant to acompliance task and/or business objective. A classification policy mayalso associate a defined tag with a classification rule. Aclassification rule defines a particular combination of criteria, suchas users who have created, accessed or modified a document or dataobject; file or application types; content or metadata keywords; clientsor storage locations; dates of data creation and/or access; reviewstatus or other status within a workflow (e.g., reviewed orun-reviewed); modification times or types of modifications; and/or anyother data attributes in any combination, without limitation. Aclassification rule may also be defined using other classification tagsin the taxonomy. The various criteria used to define a classificationrule may be combined in any suitable fashion, for example, via Booleanoperators, to define a complex classification rule. As an example, ane-discovery classification policy might define a classification tag“privileged” that is associated with documents or data objects that (1)were created or modified by legal department staff, or (2) were sent toor received from outside counsel via email, or (3) contain one of thefollowing keywords: “privileged” or “attorney” or “counsel,” or otherlike terms. Accordingly, all these documents or data objects will beclassified as “privileged.”

One specific type of classification tag, which may be added to an indexat the time of indexing, is an “entity tag.” An entity tag may be, forexample, any content that matches a defined data mask format. Examplesof entity tags might include, e.g., social security numbers (e.g., anynumerical content matching the formatting mask XXX-XX-XXXX), credit cardnumbers (e.g., content having a 13-16 digit string of numbers), SKUnumbers, product numbers, etc. A user may define a classification policyby indicating criteria, parameters or descriptors of the policy via agraphical user interface, such as a form or page with fields to befilled in, pull-down menus or entries allowing one or more of severaloptions to be selected, buttons, sliders, hypertext links or other knownuser interface tools for receiving user input, etc. For example, a usermay define certain entity tags, such as a particular product number orproject ID. In some implementations, the classification policy can beimplemented using cloud-based techniques. For example, the storagedevices may be cloud storage devices, and the storage manager 140 mayexecute cloud service provider API over a network to classify datastored on cloud storage devices.

Restore Operations from Secondary Copies

While not shown in FIG. 1E, at some later point in time, a restoreoperation can be initiated involving one or more of secondary copies116A, 116B, and 116C. A restore operation logically takes a selectedsecondary copy 116, reverses the effects of the secondary copy operationthat created it, and stores the restored data to primary storage where aclient computing device 102 may properly access it as primary data. Amedia agent 144 and an appropriate data agent 142 (e.g., executing onthe client computing device 102) perform the tasks needed to complete arestore operation. For example, data that was encrypted, compressed,and/or deduplicated in the creation of secondary copy 116 will becorrespondingly rehydrated (reversing deduplication), uncompressed, andunencrypted into a format appropriate to primary data. Metadata storedwithin or associated with the secondary copy 116 may be used during therestore operation. In general, restored data should be indistinguishablefrom other primary data 112. Preferably, the restored data has fullyregained the native format that may make it immediately usable byapplication 110.

As one example, a user may manually initiate a restore of backup copy116A, e.g., by interacting with user interface 158 of storage manager140 or with a web-based console with access to system 100. Storagemanager 140 may accesses data in its index 150 and/or managementdatabase 146 (and/or the respective storage policy 148A) associated withthe selected backup copy 116A to identify the appropriate media agent144A and/or secondary storage device 108A where the secondary copyresides. The user may be presented with a representation (e.g., stub,thumbnail, listing, etc.) and metadata about the selected secondarycopy, in order to determine whether this is the appropriate copy to berestored, e.g., date that the original primary data was created. Storagemanager 140 will then instruct media agent 144A and an appropriate dataagent 142 on the target client computing device 102 to restore secondarycopy 116A to primary storage device 104. A media agent may be selectedfor use in the restore operation based on a load balancing algorithm, anavailability based algorithm, or other criteria. The selected mediaagent, e.g., 144A, retrieves secondary copy 116A from disk library 108A.For instance, media agent 144A may access its index 153 to identify alocation of backup copy 116A on disk library 108A, or may accesslocation information residing on disk library 108A itself.

In some cases a backup copy 116A that was recently created or accessed,may be cached to speed up the restore operation. In such a case, mediaagent 144A accesses a cached version of backup copy 116A residing inindex 153, without having to access disk library 108A for some or all ofthe data. Once it has retrieved backup copy 116A, the media agent 144Acommunicates the data to the requesting client computing device 102.Upon receipt, file system data agent 142A and email data agent 142B mayunpack (e.g., restore from a backup format to the native applicationformat) the data in backup copy 116A and restore the unpackaged data toprimary storage device 104. In general, secondary copies 116 may berestored to the same volume or folder in primary storage device 104 fromwhich the secondary copy was derived; to another storage location orclient computing device 102; to shared storage, etc. In some cases, thedata may be restored so that it may be used by an application 110 of adifferent version/vintage from the application that created the originalprimary data 112.

Exemplary Secondary Copy Formatting

The formatting and structure of secondary copies 116 can vary dependingon the embodiment. In some cases, secondary copies 116 are formatted asa series of logical data units or “chunks” (e.g., 512 MB, 1 GB, 2 GB, 4GB, or 8 GB chunks). This can facilitate efficient communication andwriting to secondary storage devices 108, e.g., according to resourceavailability. For example, a single secondary copy 116 may be written ona chunk-by-chunk basis to one or more secondary storage devices 108. Insome cases, users can select different chunk sizes, e.g., to improvethroughput to tape storage devices. Generally, each chunk can include aheader and a payload. The payload can include files (or other dataunits) or subsets thereof included in the chunk, whereas the chunkheader generally includes metadata relating to the chunk, some or all ofwhich may be derived from the payload. For example, during a secondarycopy operation, media agent 144, storage manager 140, or other componentmay divide files into chunks and generate headers for each chunk byprocessing the files. Headers can include a variety of information suchas file and/or volume identifier(s), offset(s), and/or other informationassociated with the payload data items, a chunk sequence number, etc.Importantly, in addition to being stored with secondary copy 116 onsecondary storage device 108, chunk headers can also be stored to index153 of the associated media agent(s) 144 and/or to index 150 associatedwith storage manager 140. This can be useful for providing fasterprocessing of secondary copies 116 during browsing, restores, or otheroperations. In some cases, once a chunk is successfully transferred to asecondary storage device 108, the secondary storage device 108 returnsan indication of receipt, e.g., to media agent 144 and/or storagemanager 140, which may update their respective indexes 153, 150accordingly. During restore, chunks may be processed (e.g., by mediaagent 144) according to the information in the chunk header toreassemble the files.

Data can also be communicated within system 100 in data channels thatconnect client computing devices 102 to secondary storage devices 108.These data channels can be referred to as “data streams,” and multipledata streams can be employed to parallelize an information managementoperation, improving data transfer rate, among other advantages. Exampledata formatting techniques including techniques involving datastreaming, chunking, and the use of other data structures in creatingsecondary copies are described in U.S. Pat. Nos. 7,315,923, 8,156,086,and 8,578,120.

FIGS. 1F and 1G are diagrams of example data streams 170 and 171,respectively, which may be employed for performing informationmanagement operations. Referring to FIG. 1F, data agent 142 forms datastream 170 from source data associated with a client computing device102 (e.g., primary data 112). Data stream 170 is composed of multiplepairs of stream header 172 and stream data (or stream payload) 174. Datastreams 170 and 171 shown in the illustrated example are for asingle-instanced storage operation, and a stream payload 174 thereforemay include both single-instance (SI) data and/or non-SI data. A streamheader 172 includes metadata about the stream payload 174. This metadatamay include, for example, a length of the stream payload 174, anindication of whether the stream payload 174 is encrypted, an indicationof whether the stream payload 174 is compressed, an archive fileidentifier (ID), an indication of whether the stream payload 174 issingle instanceable, and an indication of whether the stream payload 174is a start of a block of data.

Referring to FIG. 1G, data stream 171 has the stream header 172 andstream payload 174 aligned into multiple data blocks. In this example,the data blocks are of size 64 KB. The first two stream header 172 andstream payload 174 pairs comprise a first data block of size 64 KB. Thefirst stream header 172 indicates that the length of the succeedingstream payload 174 is 63 KB and that it is the start of a data block.The next stream header 172 indicates that the succeeding stream payload174 has a length of 1 KB and that it is not the start of a new datablock. Immediately following stream payload 174 is a pair comprising anidentifier header 176 and identifier data 178. The identifier header 176includes an indication that the succeeding identifier data 178 includesthe identifier for the immediately previous data block. The identifierdata 178 includes the identifier that the data agent 142 generated forthe data block. The data stream 171 also includes other stream header172 and stream payload 174 pairs, which may be for SI data and/or non-SIdata.

FIG. 1H is a diagram illustrating data structures 180 that may be usedto store blocks of SI data and non-SI data on a storage device (e.g.,secondary storage device 108). According to certain embodiments, datastructures 180 do not form part of a native file system of the storagedevice. Data structures 180 include one or more volume folders 182, oneor more chunk folders 184/185 within the volume folder 182, and multiplefiles within chunk folder 184. Each chunk folder 184/185 includes ametadata file 186/187, a metadata index file 188/189, one or morecontainer files 190/191/193, and a container index file 192/194.Metadata file 186/187 stores non-SI data blocks as well as links to SIdata blocks stored in container files. Metadata index file 188/189stores an index to the data in the metadata file 186/187. Containerfiles 190/191/193 store SI data blocks. Container index file 192/194stores an index to container files 190/191/193. Among other things,container index file 192/194 stores an indication of whether acorresponding block in a container file 190/191/193 is referred to by alink in a metadata file 186/187. For example, data block B2 in thecontainer file 190 is referred to by a link in metadata file 187 inchunk folder 185. Accordingly, the corresponding index entry incontainer index file 192 indicates that data block B2 in container file190 is referred to. As another example, data block B1 in container file191 is referred to by a link in metadata file 187, and so thecorresponding index entry in container index file 192 indicates thatthis data block is referred to.

As an example, data structures 180 illustrated in FIG. 1H may have beencreated as a result of separate secondary copy operations involving twoclient computing devices 102. For example, a first secondary copyoperation on a first client computing device 102 could result in thecreation of the first chunk folder 184, and a second secondary copyoperation on a second client computing device 102 could result in thecreation of the second chunk folder 185. Container files 190/191 in thefirst chunk folder 184 would contain the blocks of SI data of the firstclient computing device 102. If the two client computing devices 102have substantially similar data, the second secondary copy operation onthe data of the second client computing device 102 would result in mediaagent 144 storing primarily links to the data blocks of the first clientcomputing device 102 that are already stored in the container files190/191. Accordingly, while a first secondary copy operation may resultin storing nearly all of the data subject to the operation, subsequentsecondary storage operations involving similar data may result insubstantial data storage space savings, because links to already storeddata blocks can be stored instead of additional instances of datablocks.

If the operating system of the secondary storage computing device 106 onwhich media agent 144 operates supports sparse files, then when mediaagent 144 creates container files 190/191/193, it can create them assparse files. A sparse file is a type of file that may include emptyspace (e.g., a sparse file may have real data within it, such as at thebeginning of the file and/or at the end of the file, but may also haveempty space in it that is not storing actual data, such as a contiguousrange of bytes all having a value of zero). Having container files190/191/193 be sparse files allows media agent 144 to free up space incontainer files 190/191/193 when blocks of data in container files190/191/193 no longer need to be stored on the storage devices. In someexamples, media agent 144 creates a new container file 190/191/193 whena container file 190/191/193 either includes 100 blocks of data or whenthe size of the container file 190 exceeds 50 MB. In other examples,media agent 144 creates a new container file 190/191/193 when acontainer file 190/191/193 satisfies other criteria (e.g., it containsfrom approx. 100 to approx. 1000 blocks or when its size exceedsapproximately 50 MB to 1 GB). In some cases, a file on which a secondarycopy operation is performed may comprise a large number of data blocks.For example, a 100 MB file may comprise 400 data blocks of size 256 KB.If such a file is to be stored, its data blocks may span more than onecontainer file, or even more than one chunk folder. As another example,a database file of 20 GB may comprise over 40,000 data blocks of size512 KB. If such a database file is to be stored, its data blocks willlikely span multiple container files, multiple chunk folders, andpotentially multiple volume folders. Restoring such files may requireaccessing multiple container files, chunk folders, and/or volume foldersto obtain the requisite data blocks.

Using Backup Data for Replication and Disaster Recovery (“LiveSynchronization”)

There is an increased demand to off-load resource intensive informationmanagement tasks (e.g., data replication tasks) away from productiondevices (e.g., physical or virtual client computing devices) in order tomaximize production efficiency. At the same time, enterprises expectaccess to readily-available up-to-date recovery copies in the event offailure, with little or no production downtime.

FIG. 2A illustrates a system 200 configured to address these and otherissues by using backup or other secondary copy data to synchronize asource subsystem 201 (e.g., a production site) with a destinationsubsystem 203 (e.g., a failover site). Such a technique can be referredto as “live synchronization” and/or “live synchronization replication.”In the illustrated embodiment, the source client computing devices 202 ainclude one or more virtual machines (or “VMs”) executing on one or morecorresponding VM host computers 205 a, though the source need not bevirtualized. The destination site 203 may be at a location that isremote from the production site 201, or may be located in the same datacenter, without limitation. One or more of the production site 201 anddestination site 203 may reside at data centers at known geographiclocations, or alternatively may operate “in the cloud.”

The synchronization can be achieved by generally applying an ongoingstream of incremental backups from the source subsystem 201 to thedestination subsystem 203, such as according to what can be referred toas an “incremental forever” approach. FIG. 2A illustrates an embodimentof a data flow which may be orchestrated at the direction of one or morestorage managers (not shown). At step 1, the source data agent(s) 242 aand source media agent(s) 244 a work together to write backup or othersecondary copies of the primary data generated by the source clientcomputing devices 202 a into the source secondary storage device(s) 208a. At step 2, the backup/secondary copies are retrieved by the sourcemedia agent(s) 244 a from secondary storage. At step 3, source mediaagent(s) 244 a communicate the backup/secondary copies across a networkto the destination media agent(s) 244 b in destination subsystem 203.

As shown, the data can be copied from source to destination in anincremental fashion, such that only changed blocks are transmitted, andin some cases multiple incremental backups are consolidated at thesource so that only the most current changed blocks are transmitted toand applied at the destination. An example of live synchronization ofvirtual machines using the “incremental forever” approach is found inU.S. Patent Application No. 62/265,339 entitled “Live Synchronizationand Management of Virtual Machines across Computing and VirtualizationPlatforms and Using Live Synchronization to Support Disaster Recovery.”Moreover, a deduplicated copy can be employed to further reduce networktraffic from source to destination. For instance, the system can utilizethe deduplicated copy techniques described in U.S. Pat. No. 9,239,687,entitled “Systems and Methods for Retaining and Using Data BlockSignatures in Data Protection Operations.”

At step 4, destination media agent(s) 244 b write the receivedbackup/secondary copy data to the destination secondary storagedevice(s) 208 b. At step 5, the synchronization is completed when thedestination media agent(s) and destination data agent(s) 242 b restorethe backup/secondary copy data to the destination client computingdevice(s) 202 b. The destination client computing device(s) 202 b may bekept “warm” awaiting activation in case failure is detected at thesource. This synchronization/replication process can incorporate thetechniques described in U.S. patent application Ser. No. 14/721,971,entitled “Replication Using Deduplicated Secondary Copy Data.”

Where the incremental backups are applied on a frequent, on-going basis,the synchronized copies can be viewed as mirror or replication copies.Moreover, by applying the incremental backups to the destination site203 using backup or other secondary copy data, the production site 201is not burdened with the synchronization operations. Because thedestination site 203 can be maintained in a synchronized “warm” state,the downtime for switching over from the production site 201 to thedestination site 203 is substantially less than with a typical restorefrom secondary storage. Thus, the production site 201 may flexibly andefficiently fail over, with minimal downtime and with relativelyup-to-date data, to a destination site 203, such as a cloud-basedfailover site. The destination site 203 can later be reversesynchronized back to the production site 201, such as after repairs havebeen implemented or after the failure has passed.

Integrating With the Cloud Using File System Protocols

Given the ubiquity of cloud computing, it can be increasingly useful toprovide data protection and other information management services in ascalable, transparent, and highly plug-able fashion. FIG. 2B illustratesan information management system 200 having an architecture thatprovides such advantages, and incorporates use of a standard file systemprotocol between primary and secondary storage subsystems 217, 218. Asshown, the use of the network file system (NFS) protocol (or any anotherappropriate file system protocol such as that of the Common InternetFile System (CIFS)) allows data agent 242 to be moved from the primarystorage subsystem 217 to the secondary storage subsystem 218. Forinstance, as indicated by the dashed box 206 around data agent 242 andmedia agent 244, data agent 242 can co-reside with media agent 244 onthe same server (e.g., a secondary storage computing device such ascomponent 106), or in some other location in secondary storage subsystem218.

Where NFS is used, for example, secondary storage subsystem 218allocates an NFS network path to the client computing device 202 or toone or more target applications 210 running on client computing device202. During a backup or other secondary copy operation, the clientcomputing device 202 mounts the designated NFS path and writes data tothat NFS path. The NFS path may be obtained from NFS path data 215stored locally at the client computing device 202, and which may be acopy of or otherwise derived from NFS path data 219 stored in thesecondary storage subsystem 218.

Write requests issued by client computing device(s) 202 are received bydata agent 242 in secondary storage subsystem 218, which translates therequests and works in conjunction with media agent 244 to process andwrite data to a secondary storage device(s) 208, thereby creating abackup or other secondary copy. Storage manager 240 can include apseudo-client manager 217, which coordinates the process by, among otherthings, communicating information relating to client computing device202 and application 210 (e.g., application type, client computing deviceidentifier, etc.) to data agent 242, obtaining appropriate NFS path datafrom the data agent 242 (e.g., NFS path information), and deliveringsuch data to client computing device 202.

Conversely, during a restore or recovery operation client computingdevice 202 reads from the designated NFS network path, and the readrequest is translated by data agent 242. The data agent 242 then workswith media agent 244 to retrieve, re-process (e.g., re-hydrate,decompress, decrypt), and forward the requested data to client computingdevice 202 using NFS.

By moving specialized software associated with system 200 such as dataagent 242 off the client computing devices 202, the architectureeffectively decouples the client computing devices 202 from theinstalled components of system 200, improving both scalability andplug-ability of system 200. Indeed, the secondary storage subsystem 218in such environments can be treated simply as a read/write NFS targetfor primary storage subsystem 217, without the need for informationmanagement software to be installed on client computing devices 202. Asone example, an enterprise implementing a cloud production computingenvironment can add VM client computing devices 202 without installingand configuring specialized information management software on theseVMs. Rather, backups and restores are achieved transparently, where thenew VMs simply write to and read from the designated NFS path. Anexample of integrating with the cloud using file system protocols orso-called “infinite backup” using NFS share is found in U.S. PatentApplication No. 62/294,920, entitled “Data Protection Operations Basedon Network Path Information.” Examples of improved data restorationscenarios based on network-path information, including using storedbackups effectively as primary data sources, may be found in U.S. PatentApplication No. 62/297,057, entitled “Data Restoration Operations Basedon Network Path Information.”

Highly Scalable Managed Data Pool Architecture

Enterprises are seeing explosive data growth in recent years, often fromvarious applications running in geographically distributed locations.FIG. 2C shows a block diagram of an example of a highly scalable,managed data pool architecture useful in accommodating such data growth.The illustrated system 200, which may be referred to as a “web-scale”architecture according to certain embodiments, can be readilyincorporated into both open compute/storage and common-cloudarchitectures.

The illustrated system 200 includes a grid 245 of media agents 244logically organized into a control tier 231 and a secondary or storagetier 233. Media agents assigned to the storage tier 233 can beconfigured to manage a secondary storage pool 208 as a deduplicationstore, and be configured to receive client write and read requests fromthe primary storage subsystem 217, and direct those requests to thesecondary tier 233 for servicing. For instance, media agents CMA1-CMA3in the control tier 231 maintain and consult one or more deduplicationdatabases 247, which can include deduplication information (e.g., datablock hashes, data block links, file containers for deduplicated files,etc.) sufficient to read deduplicated files from secondary storage pool208 and write deduplicated files to secondary storage pool 208. Forinstance, system 200 can incorporate any of the deduplication systemsand methods shown and described in U.S. Pat. No. 9,020,900, entitled“Distributed Deduplicated Storage System,” and U.S. Pat. Pub. No.2014/0201170, entitled “High Availability Distributed DeduplicatedStorage System.”

Media agents SMA1-SMA6 assigned to the secondary tier 233 receive writeand read requests from media agents CMA1-CMA3 in control tier 231, andaccess secondary storage pool 208 to service those requests. Mediaagents CMA1-CMA3 in control tier 231 can also communicate with secondarystorage pool 208, and may execute read and write requests themselves(e.g., in response to requests from other control media agentsCMA1-CMA3) in addition to issuing requests to media agents in secondarytier 233. Moreover, while shown as separate from the secondary storagepool 208, deduplication database(s) 247 can in some cases reside instorage devices in secondary storage pool 208.

As shown, each of the media agents 244 (e.g., CMA1-CMA3, SMA1-SMA6,etc.) in grid 245 can be allocated a corresponding dedicated partition251A-251I, respectively, in secondary storage pool 208. Each partition251 can include a first portion 253 containing data associated with(e.g., stored by) media agent 244 corresponding to the respectivepartition 251. System 200 can also implement a desired level ofreplication, thereby providing redundancy in the event of a failure of amedia agent 244 in grid 245. Along these lines, each partition 251 canfurther include a second portion 255 storing one or more replicationcopies of the data associated with one or more other media agents 244 inthe grid.

System 200 can also be configured to allow for seamless addition ofmedia agents 244 to grid 245 via automatic configuration. As oneexample, a storage manager (not shown) or other appropriate componentmay determine that it is appropriate to add an additional node tocontrol tier 231, and perform some or all of the following: (i) assessthe capabilities of a newly added or otherwise available computingdevice as satisfying a minimum criteria to be configured as or hosting amedia agent in control tier 231; (ii) confirm that a sufficient amountof the appropriate type of storage exists to support an additional nodein control tier 231 (e.g., enough disk drive capacity exists in storagepool 208 to support an additional deduplication database 247); (iii)install appropriate media agent software on the computing device andconfigure the computing device according to a pre-determined template;(iv) establish a partition 251 in the storage pool 208 dedicated to thenewly established media agent 244; and (v) build any appropriate datastructures (e.g., an instance of deduplication database 247). An exampleof highly scalable managed data pool architecture or so-called web-scalearchitecture for storage and data management is found in U.S. PatentApplication No. 62/273,286 entitled “Redundant and Robust DistributedDeduplication Data Storage System.”

The embodiments and components thereof disclosed in FIGS. 2A, 2B, and2C, as well as those in FIGS. 1A-1H, may be implemented in anycombination and permutation to satisfy data storage management andinformation management needs at one or more locations and/or datacenters.

Anomaly Detection and Reporting

FIG. 3 is a block diagram illustrating some salient portions of a system300 for detecting and reporting anomalies in the occurrence of events,the length of jobs, and/or the status of jobs, according to anembodiment. For example, the storage manager 140 can be configured toperform the anomaly detection and reporting.

As described herein, the storage manager 140 includes the storagemanager database 146, which can store event data and/or jobs data. Forexample, event data can include, for a particular client computingdevice 110, a history of events that have occurred on the respectiveclient computing device 110, such as the type of event, a date on whichthe event occurred, a time or time period (e.g., the hour, day, week,etc.) during which the event occurred, and the number of occurrences ofthe event during the time or time period. As an illustrative embodiment,the storage manager database 146 can store, for a particular clientcomputing device 110, event data aggregated over 1 hour periods suchthat the event data indicates the number of times a particular eventoccurs during 1 hour intervals. Alternatively, the event data can bestored locally on client computing devices 110 instead of or in additionto in the storage manager database 146. For example, a client computingdevice 110 can store event data corresponding to the client computingdevice 110 locally, and the storage manager can retrieve the event datastored on the client computing device(s) 110.

Jobs data can include, for some or all of the client computing devices110 during a particular time or time period, job status information(e.g., a number of jobs run by the client computing device(s) 110 thathave succeeded, that have failed, that have been killed (e.g., by a useror administrator), that are pending (e.g., because the jobs have yet tobe completed, the jobs are waiting for system resources, such as disksor processing threads, to become available, etc.), and/or that aresuspended (e.g., by a user or administrator)), and/or lengths of timefor jobs to complete. As an illustrative embodiment, the storage managerdatabase 146 can store, for some or all of the client computing devices110, a number of jobs run by the client computing device(s) 110 thathave succeeded, that have failed, that have been killed, that arepending, and/or that are suspended, and/or lengths of time for jobs tocomplete over 1 hour periods (e.g., the job status information is storedin 1 hour intervals and/or analyzed in 1 hour intervals to determinewhether the number of types of job statuses is anomalous).Alternatively, the jobs data can be stored in databases of one or moresecondary storage computing devices 106 instead of or in addition to inthe storage manager database 146. For example, a secondary storagecomputing device 106 can store jobs data corresponding to the secondarystorage computing device 106 locally, and the storage manager 146 canretrieve and aggregate the jobs data stored on the secondary storagecomputing device(s) 106.

Optionally, the storage manager 140 periodically prunes event dataand/or jobs data stored in the storage manager database 146. Forexample, the storage manager 140 can prune event data and/or jobs datastored in the storage manager database 146 that is older than athreshold age and/or that has been stored in the storage managerdatabase 146 for longer than a threshold time. As an illustrativeexample, the storage manager 140 can prune event data and/or jobs datathat has been stored in the storage manager database 146 longer than sixmonths.

Optionally, the storage manager 140 can aggregate event data and/or jobsdata prior to storing such data in the storage manager database 146. Forexample, the storage manager database 146 may store hourly data, andthus the storage manager 140 may aggregate any event data and/or jobsdata falling within a one hour period, and store the aggregated data inthe storage manager database 146. The storage manager 140 can alsofilter event data and/or jobs data before such data is stored in thestorage manager database 146. For example, part or all of theinformation management cell may be shut down for maintenance. During themaintenance period, no events may be recorded and/or no jobs may beinitiated or completed. To prevent the lack of events or jobs fromskewing the historical data (upon which the anomaly detectionfunctionality relies), the storage manager 140 can filter such eventdata and/or jobs data such that it does not appear as if no events orjobs occurred during the maintenance period.

As illustrated in FIG. 3 , the storage manager 140 includes an eventanomaly detector 342, a long running job detector 344, a global jobanalyzer 346, and an alert generator 348 that can provide the anomalydetection and alert functionality described herein. For example, theevent anomaly detector 342 can detect anomalies in events that occur ona particular client computing device 110. In particular, the event datacorresponding to a particular event and a particular client computingdevice 110 stored in the storage manager database 146 may be in the formof time-series data (e.g., where the x-axis represents a time or timeperiod at which the particular event occurs and the y-axis represents anumber of occurrences of the particular event during the time or timeperiod). As described herein, an event may have a seasonal pattern(e.g., have a consistent number of occurrences during particular timeperiods, such as during a certain hour, during a certain day of theweek, during a certain week of the month, etc.) and/or a trend pattern(e.g., have a number of occurrences that rise at a certain rate over aperiod of time, have a number of occurrences that fall at a certain rateover a period of time, etc.). As an illustrative example, a first typeof event that occurs on a first client computing device 110 may occurmore often on Mondays than on other days of the week (e.g., the seasonalpattern), and may increase by 10 occurrences every week (e.g., the trendpattern). An anomaly therefore may be a situation in which an event hasa number of occurrences that does not comport with the event's seasonalpattern or the event's trend pattern.

The event anomaly detector 342 can retrieve, from the storage managerdatabase 146, some or all of the event data corresponding to aparticular event that occurs on a particular client computing device110. The event anomaly detector 342 can then perform a time-seriesdecomposition on the retrieved time-series event data to separate thetime-series event data into a seasonal component, a trend component, andan error component (also referred to herein as a residual component).The seasonal component may be the portion of the time-series event datathat represents the seasonal pattern of the particular event, the trendcomponent may be the portion of the time-series event data thatrepresents the trend pattern of the particular event, and the errorcomponent may be the portion of the time-series event data thatrepresents the remaining event data for the particular event.

In some embodiments, the particular event may correspond to multipleseasonal patterns. For example, the number of occurrences of theparticular event may spike or drop during particular times of a day,during particular days of the week, and during particular weeks of amonth. In such cases, the event anomaly detector 342 can performmultiple decompositions—one for each seasonal pattern such that in eachdecomposition, the time-series event data is decomposed into a seasonalcomponent corresponding to one of the seasonal patterns, a trendcomponent, and an error component—or perform a single decomposition inwhich the time-series event data is decomposed into multiple seasonalcomponents, a trend component, and an error component.

In an embodiment, the event anomaly detector 342 can use the locallyestimated scatterplot smoothing (LOESS) process to decompose thetime-series event data. Alternatively, the event anomaly detector 342can use the Dickey Fuller test, periodogram (e.g., Fast FourierTransform), seasonal trend decomposition, the generalized extremestudentized deviate (GESD) test, and/or the like to decompose thetime-series event data. Although these mathematical techniques may beknown, their application to the present problems at hand, the sequencingof operations to find anomalies, and the combination of parametersanalyzed within the particular architectures of the illustrativeembodiments represent technological improvements over conventionalsystems.

As an illustrative example, the event anomaly detector 342 can use STL(Seasonal and Trend decomposition using Loess) to decompose atime-series into its components as follows:

-   -   1. Initialize trend as T(0)v=0 and R(0)v    -   2. Outer loop—Calculate robustness weights. Run n(o) times        -   Calculate Rv        -   Calculate robustness weights ρv=B(|Rv|/h) where            h=6*median(|Rv|) B is the bi-square weight function [1]        -   On initial loop, ρv=1    -   3. Inner loop—Iteratively calculate trend and seasonal terms.        Run n(i) times        -   Detrend: Yv−Tv(k) where k is the loop number. If the            observed value Yv is missing, then the detrended term is            also missing.        -   Cycle-subseries smoothing: The detrended time series is            broken into cyclesubseries. For example, monthly data with a            periodicity of twelve months would yield twelve            cycle-subseries, one of which would be all of the months of            January. Each cycle-subseries is then loess smoothed with            q=n(s) and d=1. The smoothed values yield a temporary            seasonal time series Ck+1.        -   Low-pass filter: The low pass filter on Ck+1 yields Lk+1.            This filter is the application of two moving averages of lag            equal to three followed by loess filtering with q=n(I) and            d=1. n(I) is defaulted the smallest odd integer greater than            the period (e.g. 13 for monthly data). The output of the            low-pass filter is Lk+1        -   Detrending of smoothed cycle-subseries: Sk+1=Ck+1−Lk+1. This            is the k+1-th estimate of seasonal component. Importantly,            the low-pass filter causes this seasonal time series to            average to be nearly zero.        -   Deseasonalizing: Y−Sk+1        -   Trend smoothing: Loess smooth the deseasonalized time series            with q=n(t). Results in Tk+1, the k+1-th estimate of the            trend component.    -   4. After obtaining univariate series, run GESD test to find        whether a given value is an outlier or not.

Once the event anomaly detector 342 has decomposed the time-series eventdata, the event anomaly detector 342 can determine a variance inassociation with the error component(s). For example, the errorcomponent(s) may represent the number of occurrences of the particularevent during various times or time periods after removing from the countthe occurrences that are attributable to the seasonal pattern(s) and/orthe trend pattern. The event anomaly detector 342 can apply a Box andWhisker analysis to the error component(s) to determine a positiveoccurrence threshold value (e.g., an upper extreme in a Box and Whiskerplot that is above the median, upper quartile, and upper whisker) that,if exceeded, indicates a possible event occurrence anomaly (e.g., theevents are occurring too often) and/or a negative occurrence thresholdvalue (e.g., a lower extreme in a Box and Whisker plot that is below themedian, lower quartile, and lower whisker) that, if not exceeded,indicates a possible event occurrence anomaly (e.g., the events are notoccurring often enough). As an illustrative example, the positiveoccurrence threshold value may be N (e.g., 1, 2, 3, 4, 5, etc.) timesthe mean, median, standard deviation, variance, etc. above the mean ormedian, and the negative occurrence threshold value may be N (e.g., 1,2, 3, 4, 5, etc.) times the mean, median, standard deviation, variance,etc. below the mean or median. Thus, if the number of occurrences of theparticular event represented by the error component(s) during aparticular time or time period exceeds the positive occurrence thresholdvalue or does not exceed the negative occurrence threshold value, thenthe event anomaly detector 342 may identify this time or time period asbeing a time or time period during which an anomaly may have occurred.The event anomaly detector 342 can provide this information to the alertgenerator 348.

Similarly, the event anomaly detector 342 can apply the Box and Whiskeranalysis to the error component(s) to identify an event anomaly in whichthe duration of time between event occurrences is too long or too short.For example, applying the Box and Whisker analysis to the errorcomponent(s) may result in creation of a minimum duration thresholdand/or a maximum duration threshold, where the minimum durationthreshold is measured between occurrences of the particular event and,if not exceeded, indicates that the particular event is occurring toooften, and where the maximum duration threshold is measured betweenoccurrences of the particular event and, if exceeded, indicates that theparticular event is not occurring often enough.

The alert generator 348 may be configured to generate an alert for anevent anomaly detected by the event anomaly detector 342. However,before generating the alert, the alert generator 348 may perform afiltering operation to determine whether an alert should be generatedfor the detected event anomaly. For example, if a particular event isoccurring or not occurring at a rate that causes the event anomalydetector 342 to indicate multiple times that the same potential anomalyis present, it may not be beneficial to user to continue to receivealerts corresponding to the anomaly given that the user has already beenalerted one or more times and the anomalies will continue to be detecteduntil the underlying issue is resolved. Thus, the alert generator 348can determine how many times an alert for the same potential anomaly hasbeen generated. If the number of generated alerts exceeds a thresholdvalue, the alert generator 348 instruct the event anomaly detector 342to raise the positive occurrence threshold value, lower the negativeoccurrence threshold value, lower the minimum duration threshold, and/orraise the maximum duration threshold for the current time or time periodof the next time or time period to be evaluated so that the eventanomaly detector 342 is less likely to indicate a possible anomaly inconjunction with future occurrences of the particular event. In otherwords, the alert generator 348 can instruct the event anomaly detector342 to adjust the range outside of which an anomaly is detected tobecome larger, resulting in fewer future anomaly detections.

Alternatively, the event anomaly detector 342 can perform the filteringoperation instead of the alert generator 348 and before indicating tothe alert generator 348 that an anomaly has been detected. In thissituation, the event anomaly detector 342 may determine whether toexpand the range outside of which an anomaly is detected. For example,the event anomaly detector 342 may make this determination based onwhether the number of alerts previously generated for the event over aperiod of time is less than a threshold (e.g., if the number of alertspreviously generated for the event is less than the threshold, then theevent anomaly detector 342 does not expand the range (e.g., increase thedifference between the occurrence thresholds and/or the durationthresholds); if the number of alerts previously generated for the eventis greater or equal to the threshold, then the event anomaly detector342 does expand the range). The event anomaly detector 342 can thendetermine whether the number of occurrences of the event (or theduration of time between event occurrences) falls within the unexpandedrange or the expanded range, as appropriate. Thus, if as a result of thefiltering operation, the range outside of which an anomaly is detectedis adjusted to be larger so that a currently evaluated number ofoccurrences of an event or future evaluated number of occurrences of anevent does not fall outside the expanded range, then the event anomalydetector 342 may not even indicate to the alert generator 348 that apotential anomaly is detected.

If the alert generator 348 determines that a detected an anomaly shouldresult in an alert, the alert generator 348 can indicate the clientcomputing device 110 on which the event occurred, the type of event forwhich an anomaly is detected, a reason why the anomaly was detected(e.g., the number of occurrences is too high because the positiveoccurrence threshold value was exceeded, the number of occurrences istoo low because the negative occurrence threshold value was notexceeded, the events are occurring too frequently in time because theduration of time between event occurrences is less than the minimumduration threshold, the events are not occurring frequently enough intime because the duration of time between event occurrences is greaterthan the maximum duration threshold, etc.), the number of occurrences ofthe event in the time or time period being evaluated, and/or the time ortime period during which the anomaly was detected (e.g., the time ortime period being evaluated).

In some embodiments, the alert generator 348 can generate one or moregraphs for inclusion in the alert. For example, the graph can depict thenumber of occurrences of the particular event over time, with anindication of the time or time period during which an anomaly isdetected.

The alert generator 348 can transmit the generated alert to the clientcomputing system 110 on which the event that caused the anomalydetection occurred. Alternatively or in addition, the alert generator348 can generate an alert as an email or other electronic message andtransmit the email or other electronic message to an email or otherelectronic message server so that a user or administrator can access theemail and alert content.

The long running job detector 344 can be configured to detect instancesin which a particular type of job running on a particular clientcomputing device 110 is running longer than expected. For example,different types of jobs can include a full secondary copy operation job,an incremental secondary copy operation job, a differential secondarycopy operation job, and/or the like. As described herein, the jobs datacan include the type of job running on the particular client computingdevice 110, the size of the secondary copy data being backed up,snapped, archived, etc., a time that the job was started, a time thatthe job finished, and/or a number of times the job was attempted untilthe job successfully completed.

The long running job detector 344 can retrieve, from the storage managerdatabase 146, the jobs data corresponding to a particular job and aparticular client computing device 110. The retrieved jobs data may berepresented by time-series indicating how long the particular job tookto complete at different time instants over a period of time, the sizeof the secondary copy data over the period of time, and/or the number ofjob attempts over the period of time. Using the jobs data, the longrunning job detector 344 can perform a time-series decomposition in amanner as described herein (e.g., in a manner similar to the time-seriesdecomposition performed by the anomaly detector 342) and analyze thedecomposed error component(s) to identify an acceptable range for thetime for the job to complete, an acceptable range for the size of thesecondary copy data being backed up, snapped, archived, etc., and/or anacceptable range for a number of job attempts. In an embodiment, theranges may be determined by the long running job detector 344 in amanner similar to how the anomaly detector 342 determines acceptableranges (e.g., the positive and negative occurrence threshold values, theminimum and maximum duration thresholds, etc.).

The long running job detector 344 can then use the acceptable ranges todetermine whether a current job or a historical job should be flagged.For example, the long running job detector 344 can flag a current orhistorical job if the time for the subject job to complete is outsidethe acceptable time range, if the size of the secondary copy data beingprocessed during the subject job is outside the acceptable size range,and/or if the number of job attempts for the subject job is outside theacceptable attempts range. If the long running job detector 344 flags acurrent or historical job, the long running job detector 344 can providethe job information to the alert generator 348.

Optionally, the anomaly detector 342 can perform the operationsdescribed herein to determine whether an event associated with thesubject job is anomalous. For example, an event associated with thesubject job may be any event that is generated in response to thesubject job being initiated, that is generated during the subject joband as a result of the subject job, and/or that is generated in responseto the subject job being completed, failing, being killed, etc. If anevent is generated that is associated with the subject job and that theanomaly detector 342 detects is anomalous, the anomaly detector 342 caninform the alert generator 348 and provide any corresponding dataidentifying the type of anomaly.

The alert generator 348 can then generate an alert, such as an alertdescribed above, if the long running job detector 344 flags a current orhistorical job and/or if the anomaly detector 342 identifies ananomalous event that is associated with the current or historical job.Optionally, the alert generator 348, the long running job detector 344,and/or the anomaly detector 342 can perform the filtering operationsdescribed herein prior to generating the alert (e.g., the long runningjob detector 344 can expand the acceptable ranges before making adetermination to flag a subject job if the number of alerts generatedpreviously for the subject job over a period of time is greater than orequal to a threshold).

In some embodiments, the alert generated by the alert generator 348 forthe current or historical job can include an indication of a possiblecause for the issue that resulted in the alert and/or a possiblesolution for resolving the issue. For example, if the long running jobdetector 344 determines that the time for the subject job to complete isoutside the acceptable time range, the long running job detector 344determines a possible cause for the subject job running long. The longrunning job detector 344 can determine whether the secondary storagecomputing device 106 running the subject job is using a deduplicationdatabase 247 to perform the subject job that is also subject to asecondary copy operation. If the deduplication database 247 is thesubject of a secondary copy operation (e.g., the deduplication database247 is being backed up) while the subject job is running, this can slowdown the subject job as various processes running as part of the subjectjob may have to wait longer for the deduplication database 247 torespond to read or write requests, further delaying other operationsthat rely on the completion of read or write requests to thededuplication database 247.

If the long running job detector 344 determines that the secondarystorage computing device 106 is running the subject job at the same timethat a secondary copy operation is being performed on the deduplicationdatabase 247 and that the subject job uses the deduplication database247, then the long running job detector 344 can determine an interval oftime at which the secondary copy operation should be performed on thededuplication database 247 and/or an interval of time when the secondarycopy operation is less likely to be performed on the deduplicationdatabase 247 such that the subject job can be recommended to run duringthis interval of time. For example, the long running job detector 344can retrieve, from the storage manager database 146, jobs dataindicating times when jobs corresponding to a secondary copy operationon the deduplication database 247 (e.g., jobs to back up thededuplication database 247) are historically run. The long running jobdetector 344 can use this jobs data to then identify periods in whichjobs corresponding to a secondary copy operation on the deduplicationdatabase 247 are not typically run, and provide this information to thealert generator 348. The alert generator 348 can then generate an alertthat includes a possible cause of the issue being that the deduplicationdatabase 247 was being backed up, snapped, archived, etc. at the sametime as the subject job and a possible suggestion on a time to run thesubject job (e.g., a time corresponding to periods in which jobscorresponding to a secondary copy operation on the deduplicationdatabase 247 are not typically run). Alternatively, the long running jobdetector 344 can use the jobs data to identify when jobs that use thededuplication database 247 are typically run, providing such informationto the alert generator 348 such that the alert generated by the alertgenerator 348 provides an indication of the possible cause (e.g., thededuplication database 247 was being backed up, snapped, archived, etc.)and suggests a time when a secondary copy operation should be run on thededuplication database 247 (e.g., a time that corresponds to times whenjobs that use the deduplication database 247 are not typically run).

Alternatively or in addition, the long running job detector 344 cancheck the query insert time of the deduplication database 247 (e.g., byretrieving jobs data from the storage manager database 146). If thequery insert time of the deduplication database 247 is higher than athreshold, then the long running job detector 344 can provide thisinformation to the alert generator 348 such that the alert generated bythe alert generator 348 indicates a possible cause of the issue (e.g.,the query insert time of the deduplication database 247 is too high)and/or a possible solution for resolving the issue.

The long running job detector 344 can also determine other possiblecauses of the subject job running too long. For example, the longrunning job detector 344 can determine whether an activity was disabled(e.g., by a user or admin) on the client computing device 110 and/or thesecondary storage computing device 106. If an activity was disabled, thelong running job detector 344 can provide this information to the alertgenerator 348 such that the alert generated by the alert generator 348can indicate a possible cause of the issue being that the activity wasdisabled and/or indicate a possible solution (e.g., enabling theactivity). As another example, the long running job detector 344 candetermine whether a secondary copy operation window was enforced (e.g.,whether a backup window was enforced, such as whether a backup occurredduring a set backup window). If the secondary copy operation window wasnot enforced (e.g., a backup occurred outside the set backup window),the long running job detector 344 can provide this information to thealert generator 348 such that the alert generated by the alert generator348 can indicate a possible cause of the issue being that the secondarycopy operation window was not enforced and/or indicate a possiblesolution (e.g., set backups to run during the secondary copy operationwindow). As another example, the long running job detector 344 candetermine whether a user or admin suspended the subject job for a time.If the subject job was suspended, the long running job detector 344 canprovide this information to the alert generator 348 such that the alertgenerated by the alert generator 348 can indicate a possible cause ofthe issue being that the subject job was suspended and/or indicate apossible solution (e.g., allow the subject job to resume if the subjectjob has not already been allowed to resume). As another example, thelong running job detector 344 can determine whether the secondary copydata being processed during the subject job has changed since the lasttime the subject job was run. If the secondary copy data has changed,the long running job detector 344 can provide this information to thealert generator 348 such that the alert generated by the alert generator348 can indicate a possible cause of the issue being that the secondarycopy data changed and/or indicate a possible solution. As anotherexample, the long running job detector 344 can determine whether a sizeof the secondary copy data being processed during the subject job hasincreased since the last time the subject job was run. If the size ofthe secondary copy data has increased, the long running job detector 344can provide this information to the alert generator 348 such that thealert generated by the alert generator 348 can indicate a possible causeof the issue being that the size of the secondary copy data hasincreased and/or indicate a possible solution.

The global job analyzer 346 can be configured to determine whether, forsome or all jobs initiated on behalf of some or all of the clientcomputing devices 110 during a particular time or time period, too manyjobs succeeded, too few jobs succeeded, too many jobs failed, too fewjobs failed, too many jobs were killed, too few jobs were killed, toomany jobs were suspended, too few jobs were suspended, too many jobs arepending, and/or too few jobs are pending. For example, the global jobanalyzer 346 can retrieve jobs data from the storage manager database146 for some or all of the jobs initiated on behalf of some or all ofthe client computing devices 110 in the cell. The jobs data may be atime-series representing the number of succeeded jobs, the number offailed jobs, the number of killed jobs, the number of suspended jobs,and/or the number of pending jobs for different time instants over aperiod of time. The global job analyzer 346 can perform a decompositionof the time-series jobs data in a manner as described herein, andanalyze the error component(s) (e.g., using Box and Whisker) todetermine an acceptance range of the number of succeeded jobs, thenumber of failed jobs, the number of killed jobs, the number ofsuspended jobs, and/or the number of pending jobs for a particular timeor time period.

The global job analyzer 346 can then determine whether the number ofsucceeded jobs, the number of failed jobs, the number of killed jobs,the number of suspended jobs, and/or the number of pending jobs at acurrent time or time period or a historical time or time period falloutside the acceptable range. If so, the global job analyzer 346 canprovide this information to the alert generator 348, and the alertgenerator 348 can generate an alert indicating which type(s) of jobsfell outside the acceptable range and/or a possible cause or reason forthe type(s) of jobs falling outside the acceptable range. For example,the alert can indicate a possible cause, such as a top failure reasonand/or correlate and highlight one component in the system 300 thatcould be the issue (e.g., a client computing device 110, a secondarystorage computing device 106, a deduplication database 247, a proxy, afirewall, etc.), if too many jobs are failing. As another example, thealert can indicate a possible cause, such as a new secondary copyoperation schedule being created (after the global job analyzer 346and/or the alert generator 348 determines that a new schedule iscreated), a secondary copy operation schedule being manually triggered(after the global job analyzer 346 and/or the alert generator 348determines that the schedule is manually triggered), and/or new clientcomputing device(s) 110 joining the system 300 (after the global jobanalyzer 346 and/or the alert generator 348 determines that new clientcomputing device(s) 110 joined the system 300), if too many jobssucceeded. As another example, the alert can indicate a possible cause,such as a high job failure, slow progress because many jobs are runninglonger than expected (which may be indicated by the long running jobdetector 344), an activity is disabled (after the global job analyzer346 and/or the alert generator 348 determines that the activity isdisabled), and/or a change to the secondary copy job operation window(e.g., a reduction in the window, which may be determined by the globaljob analyzer 346 and/or the alert generator 348), if too few jobssucceeded.

The alert generated by the alert generator 348 can also include one ormore graphs or other non-visual reports that indicate the number ofsucceeded, failed, killed, suspended, and/or pending jobs over time,with markings or other notations indicating time(s) in which at leastone of the jobs types is outside an acceptable range.

Any combination and permutation of information may be included in theissued alerts generated by the alert generator 348. In some embodiments,the information is made available through a dashboard, withoutlimitation.

Example terminology used herein is as follows:

-   -   Time series and series: a series of values of a quantity        obtained at successive times, often with equal intervals between        them.    -   Anomaly: something that deviates from what is standard, normal,        or expected.    -   Trend: a general direction in which something is developing or        changing.    -   Seasonal: relating to or characteristic of a particular season        of the time period, like daily, weekly, yearly, hourly, etc.    -   STL: Seasonal and Trend decomposition using Loess.    -   Local anomaly: Anomaly within a season. This might change an        existing seasonal pattern.    -   Global anomaly: Anomaly within a provided data set.

In general, a user or administrator can select one or more clientcomputing devices 110 and/or one or more secondary storage computingdevices 106 for which the user or administrator would like to receivealerts. The user selection can then invoke the storage manager 140 toperform the functionality described herein according to the userselection.

In some embodiments, an alert wizard (e.g., implemented by the storagemanager 140) allows the user or administrator to select an option toalert on long-running anomalous jobs. The user or administrator needs toselect an agent type. Long running jobs may use hourly seasonality. Amodel (e.g., an event anomaly detection model, a long running jobdetection model, a job status anomaly model, etc.) may be fit once in aday for the next 24 hours. The results may be cached in a table. Thealert thread may use this cached value to remove N (e.g., 1, 2, 3, 4, 6,8) seasonal values from a current running job's time. After this, thealert thread may check if the current running job's time is anomalous ornot using GESD.

An exemplary NT_STL_CacheTable may decompose values. This decompositionmay be done once every 24 hours.

-   -   Classes:        -   LoessException: to throw configuration and data related            exceptions.        -   Loess: Provides Loess based smoothing.        -   StlDecomposition: Provides STL decomposition in trend,            season and residual.

It uses helper classes.

The table may comprise decomposed values similar to the following Table1 below:

TABLE 1 Apptype Optype HourOfDay Seriese Trend Season Residual 33 4 0 12.26952 −2.31808 1.04856 33 4 1 2 2.77266 −1.49282 0.720157 33 4 2 33.26676 −0.67038 0.403618 33 4 3 4 3.7511 0.162935 0.085963 33 4 4 54.22601 1.00482 −0.23083 33 4 5 6 4.68677 1.85148 −0.53825 33 4 6 75.00241 2.73548 −0.73789 33 4 7 2 5.21541 −2.31808 −0.89733 33 4 8 35.32163 −1.49282 −0.82881 33 4 9 4 5.3273 −0.67038 −0.65692 33 4 10 55.25612 0.162935 −0.41906 33 4 11 6 5.14699 1.00482 −0.15181 33 4 12 75.04758 1.85148 0.100938 33 4 13 8 5.00227 2.73548 0.262254 33 4 14 35.03997 −2.31808 0.278108 33 4 15 4 5.17235 −1.49282 0.320464 33 4 16 55.40012 −0.67038 0.270256 33 4 17 6 5.7143 0.162935 0.12277 33 4 18 76.08956 1.00482 −0.09438 33 4 19 8 6.48252 1.85148 −0.334 33 4 20 96.8435 2.73548 −0.57898 33 4 21 4 7.13333 −2.31808 −0.81526 33 4 22 57.33106 −1.49282 −0.83824 33 4 23 6 7.42983 −0.67038 −0.75945

FIG. 4 illustrates a block diagram showing the operations performed todetect event anomalies. As illustrated in FIG. 4 , the event anomalydetector 342 can retrieve event data from the storage manager database146 for a first event and a first client computing device (e.g., for afirst event that occurred on a first client computing device) at (1).For example, the event anomaly detector 342 can retrieve the event datain response to a request by a user or administrator to determine whetherany anomalous events are occurring. As another example, the eventanomaly detector 342 can retrieve the event data automatically foranalysis, such as in periodic intervals or when a new event is receivedor detected.

The event anomaly detector 342 can perform a time-series decompositionof the event data at (2). For example, the event anomaly detector 342can apply the LOESS process, Dickey Fuller test, periodogram, the GESDtest, and/or the like to decompose the time-series event data.

The event anomaly detector 342 can then analyze a component of thetime-series (e.g., the seasonal component(s), the trend component,and/or the error component) to detect a potential anomaly at (3). Forexample, the event anomaly detector 342 can apply a Box and Whiskeranalysis to the error component to determine whether the eventoccurrences at a particular time or time period exceeds an upper extremeof a Box and Whisker plot or does not exceed a lower extreme of the Boxand Whisker plot, indicating a possible anomaly. In an embodiment, theupper extreme and/or the lower extreme may be N (e.g., 1, 2, 3, 4, 5,etc.) times the mean, median, standard deviation, variance, etc. awayfrom the mean or median.

The event anomaly detector 342 can then transmit anomaly information tothe alert generator 348 at (4). For example, the anomaly information maybe an indication of the client computing device 110 on which the eventoccurred, the type of event, the time or time period during which theevent occurred, the threshold that was exceeded (e.g., a durationthreshold or an occurrence threshold), a number of occurrences of theevent during the time or time period, and/or the type of anomaly (e.g.,a frequency anomaly, in which a duration threshold was violated, or anoccurrence anomaly, in which an occurrence threshold was violated).

The alert generator 348 can determine whether to filter the anomaly at(5). For example, the alert generator 348 can perform a filteringoperation to determine whether a sufficient number of alerts have beengenerated for the same event, which may indicate that the user oradministrator is already aware of the issue. Instead of sending aduplicate alert that provides the user or administrator with no newinformation, the alert generator 348 can filter the anomaly and notgenerate an alert. The filtering may occur in the form of instructingthe event anomaly detector 342 to adjust the duration and/or occurrencethresholds (e.g., either all the thresholds or just the threshold thatwas violated) such that future event occurrences that are analyzed areless likely to violate the set duration and/or occurrence thresholds.Alternatively, the event anomaly detector 342 can perform the filteringoperation instead of the alert generator 348. In this case, the eventanomaly detector 342 may not even inform the alert generator 348 of theanomaly information if the anomaly is filtered (such as if the anomalyis filtered because the number of occurrences of the event or theduration of time between events now falls within an expanded range, whenthe number of occurrences or duration would not fall within theunexpanded range).

The alert generator 348 can generate an alert for the anomaly if theanomaly is not filtered at (6). The alert can be an email or otherelectronic message. The alert can include a table with anomalyinformation, one or more graphs depicting event occurrences at timeinstances over a period of time (with or without markings or annotationsindicating which time instant(s) correspond to a detected anomaly),and/or other visual or non-visual information.

While FIG. 4 depicts the operations being performed in a specific order,this is not meant to be limiting. The operations described above withrespect to FIG. 4 can be performed in any order.

Optionally, the event anomaly detector 342 can repeat the aboveoperations for any number or types of events that occur on any number ofdifferent client computing devices 110.

FIG. 5 illustrates a block diagram showing the operations performed todetect long running jobs. As illustrated in FIG. 5 , the long runningjob detector 344 retrieves jobs data from the storage manager database146 for a first job and a first client computing device (e.g., for afirst job initiated by or otherwise corresponding to a first clientcomputing device) at (1). For example, the long running job detector 344can retrieve the jobs data in response to a request by a user oradministrator to determine whether any jobs initiated by or otherwisecorresponding to a particular client computing device 110 are runninglong or have other issues. As another example, the long running jobdetector 344 can retrieve the jobs data automatically for analysis, suchas in periodic intervals or when a new job is received or detected.

The long running job detector 344 can perform a time-seriesdecomposition of the jobs data at (2). For example, the long running jobdetector 344 can apply the LOESS process, Dickey Fuller test,periodogram, the GESD test, and/or the like to decompose the time-seriesevent data.

The long running job detector 344 can then analyze a component of thetime-series (e.g., the seasonal component(s), the trend component,and/or the error component) to determine an acceptable range for time toperform the first job, for a secondary copy data size being processed bythe first job, and/or for a number of job attempts at (3). For example,long running job detector 344 can apply a Box and Whisker analysis tothe error component to determine the acceptable ranges, where theboundaries of the acceptable ranges may be an upper extreme of a Box andWhisker plot and a lower extreme of the Box and Whisker plot. In anembodiment, the upper extreme and/or the lower extreme may be N (e.g.,1, 2, 3, 4, 5, etc.) times the mean, median, standard deviation,variance, etc. away from the mean or median.

The long running job detector 344 can then determine a possible cause ifthe first job is running longer than the acceptable time range at (4).For example, a possible cause could be that the deduplication database247 being used by the first job is being backed up while the first jobis running, a user or administrator may have disabled an activity, asecondary copy operation window may not be enforced, a user oradministrator may have suspended the first job, the secondary copy databeing processed by the first job may have changed in file or contentmakeup, and/or the size of the secondary copy data being processed bythe first job may have increased. The long running job detector 344 canalso otherwise flag the first job if any of the acceptable ranges isviolated.

The long running job detector 344 can then transmit the acceptableranges and/or the possible cause to the alert generator 348 at (5),optionally along with an indication of whether the first job has beenflagged. Before, during, and/or after operations (2) through (5) areperformed, the event anomaly detector 342 can determine whether anyevents corresponding to the first job are anomalous at (6). For example,an event corresponding to the first job may be an event that occurs whenthe first job is initiated, an event that occurs during performance ofthe first job and that occurs as a result of the first job, and/or anevent that occurs when the first job completes, fails, is killed, and/oris suspended.

The event anomaly detector 342 can then transmit anomaly information tothe alert generator 348 at (7). For example, the anomaly information maybe an indication of the client computing device 110 on which a detectedanomalous event occurred, the type of event, the time or time periodduring which the detected anomalous event occurred, the threshold thatwas exceeded (e.g., a duration threshold or an occurrence threshold), anumber of occurrences of the detected anomalous event during the time ortime period, and/or the type of anomaly (e.g., a frequency anomaly, inwhich a duration threshold was violated, or an occurrence anomaly, inwhich an occurrence threshold was violated).

The alert generator 348 can determine whether to generate an alert at(8). For example, the alert generator 348 can perform the filteroperation described above in determining whether to generate an alert.Alternatively, the event anomaly detector 342 and/or the long runningjob detector 344 can perform the filtering operation instead of thealert generator 348. In this case, the event anomaly detector 342 maynot even inform the alert generator 348 of the anomaly informationand/or the long running job detector 344 may not provide the acceptableranges and/or possible cause if the flagged job or anomaly is filtered.If the alert generator 348 generates an alert, the alert can include atable with anomaly information, acceptable ranges and/or ranges thatwere violated, one or more graphs depicting event occurrences at timeinstances over a period of time (with or without markings or annotationsindicating which time instant(s) correspond to a detected anomaly), oneor more graphs depicting jobs data at time instances over a period oftime, and/or other visual or non-visual information.

While FIG. 5 depicts the operations being performed in a specific order,this is not meant to be limiting. The operations described above withrespect to FIG. 5 can be performed in any order.

Optionally, the long running job detector 344 can repeat the aboveoperations for any number or types of jobs associated with any number ofdifferent client computing devices 110.

FIG. 6 illustrates a block diagram showing the operations performed todetect job statuses that are occurring too often or less often. Asillustrated in FIG. 6 , the global job analyzer 346 retrieves jobs datafrom the storage manager database 146 for some or all client computingdevices (e.g., for some or all jobs initiated by or otherwisecorresponding to some or all of the client computing devices) at (1).For example, the global job analyzer 346 can retrieve the jobs data inresponse to a request by a user or administrator to determine whetherany job statuses are occurring at an unusual rate. As another example,the global job analyzer 346 can retrieve the jobs data automatically foranalysis, such as in periodic intervals or when a new job or set of jobsis received or detected.

The global job analyzer 346 can perform a time-series decomposition ofthe jobs data at (2). For example, the global job analyzer 346 can applythe LOESS process, Dickey Fuller test, periodogram, seasonal trenddecomposition, the GESD test, and/or the like to decompose thetime-series event data.

The global job analyzer 346 can then analyze a component of thetime-series (e.g., the seasonal component(s), the trend component,and/or the error component) to determine an acceptable range forsucceeded, failed, killed, suspended, and/or pending jobs at (3). Forexample, global job analyzer 346 can apply a Box and Whisker analysis tothe error component to determine the acceptable ranges, where theboundaries of the acceptable ranges may be an upper extreme of a Box andWhisker plot and a lower extreme of the Box and Whisker plot. In anembodiment, the upper extreme and/or the lower extreme may be N (e.g.,1, 2, 3, 4, 5, etc.) times the mean, median, standard deviation,variance, etc. away from the mean or median.

The global job analyzer 346 can then determine a possible cause if thesucceeded, failed, killed, suspended, and/or pending jobs fall outsidethe acceptable ranges at (4). For example, a possible cause could be acomponent in the system 300 (e.g., a client computing device 110, asecondary storage computing device 106, a secondary storage device 108,a deduplication database 247, a proxy, a firewall, etc.) failing ormalfunctioning, a new secondary copy operation schedule being created, asecondary copy operation schedule being manually triggered by a user oradministrator, new client computing devices 110 being added to thesystem 300, a high number of job failures, slow progress because manyjobs are running slowly, an activity is disabled, a secondary copyoperation window is adjusted (to be larger or smaller), and/or the like.The global job analyzer 346 can also otherwise indicate which jobstatus(es) fall outside an acceptable range.

The global job analyzer 346 can then transmit the acceptable rangesand/or the possible cause to the alert generator 348 at (5), optionallyalong with an indication of which job statuses appear to be anomalous.

Optionally, the alert generator 348 can determine whether to generate analert. For example, the alert generator 348 can perform the filteroperation described above in determining whether to generate an alert.Alternatively, the global job analyzer 346 can perform the filteringoperation instead of the alert generator 348. In this case, the globaljob analyzer 346 may not even inform the alert generator 348 of ananomalous job status if the anomalous job status is filtered.

If the anomalous job status is not filtered, the alert generator 348 cangenerate one or more graphs corresponding to the anomalous jobstatus(es) at (6). For example, the graph(s) can depict the number ofdifferent types of job statuses (e.g., the number of succeeded jobs,failed jobs, killed jobs, suspended jobs, and/or pending jobs) atdifferent time instants over a period of time, with markings orannotations indicating which time instants correspond to anomalous jobstatuses and/or which job statuses are anomalous. The alert generator348 can then generate an alert that includes the generated graph(s) at(7). In addition to the graph(s), the alert can include a table with jobstatus information, acceptable ranges and/or ranges that were violated,and/or other visual or non-visual information.

While FIG. 6 depicts the operations being performed in a specific order,this is not meant to be limiting. The operations described above withrespect to FIG. 6 can be performed in any order.

Optionally, the global job analyzer 346 can repeat the above operationsfor any number of times or time periods.

FIG. 7 depicts some salient operations of a method 700 for detecting ananomalous event, according to an embodiment. The method 700 may beimplemented, for example, by a storage manager, such as the storagemanager 140. The method 700 may start at block 702.

At block 702, event data for a first event and a first client computingdevice is retrieved. For example, the event data may be historical dataindicating the number of occurrences of the first event at differenttime instants over a period of time.

In some embodiments, the event data is retrieved from the storagemanager database 146. In other embodiments, the event data is retrievedfrom the first client computing device.

At block 704, a time-series decomposition of the event data isperformed. The decomposition may result in one or more seasonalcomponent(s), a trend component, and an error component. Alternatively,multiple decompositions can be performed, with each decompositionresulting in a different seasonal component, trend component, and errorcomponent.

At block 706, a component of the time-series is analyze to detect ananomaly. For example, the error component of the time-series can beanalyzed to detect the anomaly. If multiple decompositions areperformed, then each error component of each decomposition may beanalyzed separately to detect an anomaly.

As part of the anomaly detection, a Box and Whisker analysis can beperformed on the error component to determine upper and lower extremes.If the occurrence of the first event at a particular time instant fallsbelow the lower extreme (e.g., the negative occurrence threshold value)or above the upper extreme (e.g., the positive occurrence thresholdvalue), then this may indicate that an anomaly is present. Similarly, ifthe duration between occurrences of the first event falls below thelower extreme (e.g., the minimum duration threshold) or above the upperextreme (e.g., the maximum duration threshold), then this may indicatethat an anomaly is present. The anomaly can be an occurrence anomaly(e.g., too many or too few occurrences) or a frequency anomaly (e.g.,the duration between occurrences is too short or too long).

At block 708, whether to filter the anomaly is determined. For example,the anomaly may be filtered if the user or administrator has alreadybeen notified of the same anomaly one or more times. As another example,the anomaly may be filtered if the time it takes to generate the alert,to transmit the alert to the user or administrator, to have the user oradministrator read the alert, and to have the user or administrator takeappropriate action to resolve the alert is longer than the time it takesfor a job to complete or an event occurrence to be increased ordecreased, as appropriate. As another example, the anomaly may befiltered if the difference between a number of occurrences thattriggered a previous alert with respect to the same event and a numberof occurrences that is triggering a current alert with respect to thesame event is less than a threshold percentage value.

Filtering the anomaly can be implemented by adjusting the upper and/orlower extremes determined during the analysis of the error componentsuch that it is less likely that the number of future event occurrenceswill fall outside the extremes. The upper and/or lower extremes can beadjusted periodically over time such that the likelihood of a number ofoccurrences resulting in an alert in the future is gradually reduced. Inthis way, the anomalies may not be stopped suddenly or only after a setnumber of alerts have been generated, but rather may be stoppedgradually and at a pace that is determined by the values of the extremesand how much the extremes are increased or decreased during each filterstage. The rate at which the extremes are increased or decreased duringeach filter stage can be preset or set by the user or administrator.

In some embodiments, a user or administrator can indicate that an alertshould be silenced or that an alert is not wanted, in which case thealert may not be generated.

At block 710, an alert is generated for the anomaly if the anomaly isnot filtered. The alert can be transmitted to a user or administrator asa push notification, a pop-up message, an email, a text message, and/orany other electronic message.

The alert can include information identifying what triggered the anomalydetection, a possible cause, and/or a possible resolution. After thealert is generated, the method 700 is complete.

FIG. 8 depicts some salient operations of a method 800 for detectinglong running jobs, according to an embodiment. The method 800 may beimplemented, for example, by a storage manager, such as the storagemanager 140. The method 800 may start at block 802.

At block 802, jobs data for a first job and a first client computingdevice is retrieved. For example, the jobs data may be historical dataindicating the job length, secondary copy data size, and/or number ofjob attempts for performing the first job at different time instantsover a period of time. The first job may not refer to specific job thatruns at a specific time instant only. Rather, the first job may refer toa specific type of job that runs periodically to process a similar orsame subset of data. For example, a first job may be an incrementalbackup job to back up files of a first application, a full backup job toback up files of a first application, an incremental backup job to backup a file system, an incremental snapshot job to take a snapshot of avolume, etc.

In some embodiments, the jobs data is retrieved from the storage managerdatabase 146. In other embodiments, the jobs data is retrieved from thefirst client computing device.

At block 804, a time-series decomposition of the jobs data is performed.The decomposition may result in one or more seasonal component(s), atrend component, and an error component. Alternatively, multipledecompositions can be performed, with each decomposition resulting in adifferent seasonal component, trend component, and error component.

At block 806, a component of the time-series is analyze to detect anacceptable range of time to perform the first job, for a secondary copydata size of secondary copy data being processed by the first job,and/or for a number of job attempts to perform the first job. Forexample, the error component of the time-series can be analyzed todetect the acceptable ranges. If multiple decompositions are performed,then each error component of each decomposition may be analyzedseparately to detect the acceptable ranges.

As part of the acceptable range detection, a Box and Whisker analysiscan be performed on the error component to determine upper and lowerextremes. The upper and lower extremes may represent the boundaries ofthe acceptable range.

At block 808, a possible cause for the first job running long isdetermined in response to the first job running longer than theacceptable time range. Determination of the possible cause can includeanalyzing jobs corresponding to a deduplication database 247 used by thefirst job to determine whether the deduplication database 247 is beingbacked up at the same time that the first job is running.

Other possible causes can include an activity being disabled, asecondary copy operation window not being enforced, a user oradministrator suspending the first job, the content of the secondarycopy data being processed by the first job changing, and/or the size ofthe secondary copy data increasing. Possible solutions to resolve theissue can also be determined.

At block 810, whether any events corresponding to the first job areanomalous is determined. For example, an event corresponding to thefirst job may be an event that occurs as a result of an action taken bythe first job and/or as a result of a status of the first job changing.

To detect whether an event corresponding to the first job is anomalous,the method 800 can perform some or all of the operations described abovewith respect to the method 700. Block 810 can be performed before,during, and/or after blocks 804 through 808 are performed.

At block 812, whether to generate an alert is determined. An alert maybe generated if the time to perform the first job at a particular timeinstant or time period falls outside the acceptable time range, if thesize of the secondary copy data processed by the first job at aparticular time instant or time period falls outside the acceptable sizerange, if the number of job attempts to perform the first job at aparticular time instant or time period falls outside the acceptableattempts range, and/or if an event corresponding to the first job isdetected as being anomalous.

Optionally, a filtering operation can be performed before an alert isgenerated. The filtering operation may result in no alert beinggenerated even if the time to perform the first job at a particular timeinstant or time period falls outside the acceptable time range, if thesize of the secondary copy data processed by the first job at aparticular time instant or time period falls outside the acceptable sizerange, if the number of job attempts to perform the first job at aparticular time instant or time period falls outside the acceptableattempts range, and/or if an event corresponding to the first job isdetected as being anomalous.

In some embodiments, a user or administrator can indicate that an alertshould be silenced or that an alert is not wanted, in which case thealert may not be generated. After the determination is made, the method800 is complete.

FIG. 9 depicts some salient operations of a method 900 for detectinganomalous job statuses, according to an embodiment. The method 900 maybe implemented, for example, by a storage manager, such as the storagemanager 140. The method 900 may start at block 902.

At block 902, jobs data for some or all of the jobs initiated by orotherwise associated with some or all of the client computing devices isretrieved. For example, the jobs data may be historical data indicatingthe number of succeeded, failed, killed, suspended, and/or pending jobsat different time instants over a period of time.

In some embodiments, the jobs data is retrieved from the storage managerdatabase 146. In other embodiments, the jobs data is retrieved from thefirst client computing device.

At block 904, a time-series decomposition of the jobs data is performed.The decomposition may result in one or more seasonal component(s), atrend component, and an error component. Alternatively, multipledecompositions can be performed, with each decomposition resulting in adifferent seasonal component, trend component, and error component.

At block 906, a component of the time-series is analyzed to detect anacceptable range for the number of succeeded, failed, killed, suspended,and/or pending jobs. For example, the error component of the time-seriescan be analyzed to detect the acceptable ranges. If multipledecompositions are performed, then each error component of eachdecomposition may be analyzed separately to detect the acceptableranges.

As part of the acceptable range detection, a Box and Whisker analysiscan be performed on the error component to determine upper and lowerextremes. The upper and lower extremes may represent the boundaries ofthe acceptable range.

At block 908, a possible cause is determined in response to the numberof succeeded, failed, killed, suspended, and/or pending jobs fallingoutside the acceptable ranges. Possible causes can include a componentof the system 300 failing or malfunctioning, a new secondary copyoperation schedule being created, a secondary copy operation schedulebeing manually triggered, such as a time that the secondary copyoperation is not normally performed, new client computing devices 110being added to the system 300, a high job failure rate, slow progressbecause many jobs are running longer than expected, a user oradministrator disabling an activity and forgetting to re-enable theactivity, and/or a change to the secondary copy operation window (e.g.,increased or decreased).

Possible solutions to resolve the issue can also be determined. Thepossible solutions may be to reverse actions identified as beingpossible causes.

At block 910, one or more graphs corresponding to the anomalous jobstatuses is generated. The graph(s) can depict the number of differenttypes of job statuses (e.g., the number of succeeded jobs, failed jobs,killed jobs, suspended jobs, and/or pending jobs) at different timeinstants over a period of time, with markings or annotations indicatingwhich time instants correspond to anomalous job statuses and/or whichjob statuses are anomalous.

At block 912, an alert that includes the generated graph(s) isgenerated. In addition to the graph(s), the alert can include a tablewith job status information, acceptable ranges and/or ranges that wereviolated, and/or other visual or non-visual information.

Optionally, a filtering operation can be performed before an alert isgenerated. The filtering operation may result in no alert beinggenerated even if the number of succeeded, failed, killed, suspended,and/or pending jobs falling outside the acceptable ranges.

In some embodiments, a user or administrator can indicate that an alertshould be silenced or that an alert is not wanted, in which case thealert may not be generated. After the alert is generated, the method 900is complete.

FIGS. 10A-10B depict a graphical user interface 1000 showing an anomalynotification or alert, according to an embodiment. The graphical userinterface 1000 can be generated by the storage manager 140 or anothercomponent in the system 300. As illustrated in FIGS. 10A-10B, the alertmay be generated as a result of the storage manager 140 (e.g., the longrunning job detector 344) detecting that one or more jobs are runninglonger than usual.

The graphical user interface 1000 includes a table 1002 that providesinformation on jobs that have been detected as running longer thanusual. For example, the table 1002 identifies a server (or cell) onwhich the long running job was running; the subclient (e.g., clientcomputing device 110) that initiated the long running job, on which thelong running job was running, or that is otherwise associated with thejob; the job ID of the long running job; the current (as of generationof the alert) completion percentage of the long running job; the anomalythreshold that triggered a detection of a long running job; a runningtime of the long running job; and a reason for the delay, includingpossible causes and/or suggestions for resolving the issue.

FIG. 11A depicts another graphical user interface 1100 showing ananomaly notification or alert, according to an embodiment. The graphicaluser interface 1100 can be generated by the storage manager 140 oranother component in the system 300. As illustrated in FIG. 11A, thealert may be generated as a result of the storage manager 140 (e.g., theglobal job analyzer 346) detecting that the number of at least one typeof job status is unusually high or low. Specifically, the graphical userinterface 1100 provides a notice indicating that an unusually largenumber of jobs in the system 300 have failed, providing the current (asof generation of the alert) number of failed jobs (e.g., 109) and theexpected number of failed jobs (e.g., less than 28, which may be themean or median number of failed jobs or the upper extreme).

FIG. 11B depicts another graphical user interface 1150 showing ananomaly notification or alert, according to an embodiment. The graphicaluser interface 1150 can be generated by the storage manager 140 oranother component in the system 300. As illustrated in FIG. 11B, thealert may be generated as a result of the storage manager 140 (e.g., theglobal job analyzer 346) detecting that the number of at least one typeof job status is unusually high or low. Specifically, the graphical userinterface 1150 provides a notice indicating that an unusually largenumber of jobs in the system 300 are pending, providing the current (asof generation of the alert) number of pending jobs (e.g., 17) and theexpected number of pending jobs (e.g., less than 16, which may be themean or median number of failed jobs or the upper extreme).

FIG. 12 depicts another graphical user interface 1200 showing an anomalynotification or alert, according to an embodiment. The graphical userinterface 1200 can be generated by the storage manager 140 or anothercomponent in the system 300. As illustrated in FIG. 12 , the alert maybe generated as a result of the storage manager 140 (e.g., the eventanomaly detector 342) detecting that one or more events are anomalous.

The graphical user interface 1200 includes a table 1202 that providesinformation on the events detected as being anomalous. For example, thetable 1202 identifies a server (or cell) on which the anomalous eventoccurred; a time that the anomalous event occurred; details explainingthe type of event that is detected as being anomalous, a number ofoccurrences of the anomalous event at the time or time period, and atype of anomaly that was detected (e.g., occurrence anomaly or frequencyanomaly).

FIG. 13 depicts another graphical user interface 1300 showing an anomalynotification or alert, according to an embodiment. The graphical userinterface 1300 can be generated by the storage manager 140 or anothercomponent in the system 300. As illustrated in FIG. 13 , the alert maybe generated as a result of the storage manager 140 (e.g., the globaljob analyzer 346) detecting that the number of at least one type of jobstatus is unusually high or low. Specifically, the graphical userinterface 1300 provides a notice indicating that an unusually fewernumber of jobs succeeded in the system 300 during a particular timeperiod (e.g., 7 AM to 8 AM on Aug. 28, 2019), providing the current (asof generation of the alert) number of succeeded jobs (e.g., 0).

The graphical user interface 1300 includes a graph 1302 that providesinformation on history of the number of succeeded jobs in the system 300over a period of time (e.g., the last 24 hours). The graph 1302 furtherincludes a marker 1304 indicating the time or time period at which thenumber of succeeded jobs fell below an acceptable range.

FIG. 14 depicts another graphical user interface 1400 showing an anomalyjob dashboard, according to an embodiment. The graphical user interface1400 can be generated by the storage manager 140 or another component inthe system 300. As illustrated in FIG. 14 , the dashboard depicted inthe graphical user interface 1400 can provide, in tab 1410, a high leveloverview of the number of jobs succeeded, failed, killed, suspended,and/or pending in a cell (e.g., 7), and the number of client computingdevices 110 in the cell (e.g., 4).

The graphical user interface 1400 can further include, in the tab 1410,a pie chart 1402 depicting the percentage of jobs succeeded, failed,killed, suspended, and/or pending that correspond to the system 300 orcell (e.g., 100%), and a pie chart 1404 depicting reasons why jobs arelong running, such as a percentage of jobs that are delayed or longrunning for a first reason (e.g., 81% of the jobs that are long runningare waiting for other services), a percentage of jobs that are delayedor long running for a second reason (e.g., 19% of jobs that are longrunning do not have any available streams allocated thereto), apercentage of jobs that are delayed or long running for a third reason,and so on. The tab 1410 further includes a table 1406 providing moreinformation on the anomalous jobs and/or the jobs corresponding toanomalous job statuses, including server, client, subclient, agent, jobID, state or status, percentage of the job that is complete, the reasonfor delay, and the duration of the job.

Tab 1420 in the graphical user interface 1400, not shown, can depictsimilar information for anomalous events. For example, the tab 1420 candepict a list of anomalous events and their details, causes for theanomalous events and a percentage of each cause shown in a pie chartsimilar to the pie chart 1404, other graphs or tables showing anomalousevent information, and/or the like.

Anomaly Detection of Deduplication Pruning Operations

In an embodiment, the storage manager 140 can also perform anomalydetection on deduplication database 247 pruning operations. For example,an archive file may be generated in response to a job being performed(e.g., a backup job) and the archive file may comprise various chunks.Each chunk may store a data block and/or a reference to a data blockthat was already stored in another chunk (possibly in another archivefile). Information about archive files and their corresponding chunksmay be stored in the storage manager database 146. The deduplicationdatabase 247 may store or have access to a table that indicates, for adata block, a signature of the data block, an archive file or chunk inwhich the data block is stored, a reference count of a number of timesthe data block is referenced by other chunks or archive files, and/orthe other chunks or archive files that reference the data block.Generally, to perform pruning of chunks, the deduplication database 247may receive a list of archive files that are to be deleted, and thededuplication database 247 can update the table to reduce the referencecount as appropriate (e.g., reduce the reference count for data blocksthat are referenced in chunks of archive files to be deleted). Thechunks of the archive files may not be deleted immediately, however,because the chunks may include data blocks referenced by other chunks.Thus, the deduplication database 247 can then, after updating the table,provide the storage manager 140 with a list of archive files that onlyinclude data blocks for which the reference count is 0. The storagemanager 140 or another component in the system 100 can then delete thechunks of these listed archive files.

In some circumstances, a delay can occur in the transmission of the listof archive files that are to be deleted to the deduplication database247, resulting in a backlog of archive file deletion indications thatthe deduplication database 247 needs to process to update the table. Inother circumstances, the deduplication database 247 can be runningslowly (e.g., because the deduplication database 247 is being backed up,a media agent 144 is running slowly or has failed, etc.) such that thereis a delay in generation of the list of archive files that only includedata blocks for which the reference count is 0. Thus, archive files(e.g., chunks) that could otherwise be deleted are not deleted, reducingthe amount of available memory space to store other blocks.

Accordingly, the storage manager 140 can store, for various timeperiods, a count (e.g., a backlog) of the number of archive files orchunks that a deduplication database 247 has yet to process to updatethe table, a count of the number of archive files or chunks identifiedas only having data blocks for which the reference count is 0, and/or atime since the last list of archive files that only include data blocksfor which the reference count is 0 was generated by the deduplicationdatabase 247. The event anomaly detector 342 can retrieve thisdeduplication pruning information from the deduplication database 247and implement the anomaly detection and reporting functionalitydescribed herein to detect whether there are any anomalous delays in thepruning operations of the deduplication database 247 (e.g., any delaysin the processing of archive files to be deleted and/or any delays inthe generation of the list of archive files or chunks to delete becausethey include only data blocks having a reference count of 0). In someembodiments, instead of comparing the absolute value of the counts ortimes over various time periods, the event anomaly detector 342 candetermine a difference in the absolute value of the counts or timesbetween time periods (e.g., between 1 minute periods, between 1 hourperiods, between 1 day periods, between 1 week periods, etc.), and usethe determined differences to detect anomalies and/or generate alerts.Generally, if the difference is negative, the event anomaly detector 342likely will not identify anomalous activity. In other embodiments, theevent anomaly detector 342 uses the absolute value of the counts ortimes to detect anomalies and/or generate alerts.

FIG. 15 depicts some salient operations of a method 1500 for detectinganomalous delays in the pruning operations of a deduplication database,such as the deduplication database 247, according to an embodiment. Themethod 1500 may be implemented, for example, by a storage manager, suchas the storage manager 140. The method 1500 may start at block 1502.

At block 1502, deduplication pruning information is retrieved. Forexample, the deduplication pruning information can include, for one ormore time periods (e.g., for one or more deduplication pruningoperations), a count (e.g., a backlog) of the number of archive files orchunks that a deduplication database 247 has yet to process to updatethe table (e.g., the table that indicates, for a data block, a signatureof the data block, an archive file or chunk in which the data block isstored, a reference count of a number of times the data block isreferenced by other chunks or archive files, and/or the other chunks orarchive files that reference the data block), a count of the number ofarchive files or chunks identified as only having data blocks for whichthe reference count is 0, and/or a time since the last list of archivefiles that only include data blocks for which the reference count is 0was generated by the deduplication database 247. A time period can be a1 minute period, 1 hour period, 1 day period, 1 week period, etc. Theportion of the deduplication pruning information corresponding to aparticular time period may be derived from a particular deduplicationpruning operation.

In some embodiments, the deduplication pruning information is retrievedfrom one or more deduplication databases 247. In other embodiments, thededuplication pruning information is retrieved from the storage managerdatabase 146.

At block 1504, a time-series decomposition of the deduplication pruninginformation is performed. The decomposition may result in one or moreseasonal component(s), a trend component, and an error component.Alternatively, multiple decompositions can be performed, with eachdecomposition resulting in a different seasonal component, trendcomponent, and error component.

At block 1506, a component of the time-series is analyzed to detect anacceptable range for a time to process archive files to be deletedand/or for a time to generate a list of archive files or chunks todelete. For example, the error component of the time-series can beanalyzed to detect the acceptable ranges. If multiple decompositions areperformed, then each error component of each decomposition may beanalyzed separately to detect the acceptable ranges.

As part of the acceptable range detection, a Box and Whisker analysiscan be performed on the error component to determine upper and lowerextremes. The upper and lower extremes may represent the boundaries ofthe acceptable range.

In some embodiments, the acceptable ranges are absolute time values. Inother embodiments, the acceptable ranges are delta time values (e.g., adifferent in absolute time values). For example, an acceptable range maybe a delta time value representing a difference between a time toprocess archive files to be deleted in a first time period and a time toprocess archive files to be deleted in a successive time period. Anacceptable range may be a delta time value that is a positive numberless than a threshold difference. In further embodiments, a negativedelta time value may be considered to fall within an acceptable range.However, a negative delta time value may also fall outside an acceptablerange in some circumstances (e.g., if data corruption causes the loss ofthe list of archive files to delete such that fewer archive files aredeleted than expected).

At block 1508, a possible cause is determined in response to the time toprocess archive files to be deleted and/or the time to generate a listof archive files or chunks to delete falling outside the acceptableranges. Possible causes can include a component of the system 300failing or malfunctioning (e.g., the deduplication database 247, a mediaagent 144, etc.), a delay in the transmission of the list of archivefiles that are to be deleted from, for example, a client computingdevice 110 to the deduplication database 247, a backup or restore of thededuplication database 247 occurring during the time period, a backup orrestore of a media agent 144 occurring during the time period, a mediaagent 144 running slowly or about to fail, a user or administratordisabling an activity and forgetting to re-enable the activity, datacorruption causing the loss of at least a portion of the list of archivefiles to delete, data corruption causing the loss of other data usefulin determining which archive files to delete, etc. The possible causecan be presented to an administrator or user in a user interfacerendered and/or displayed by a client computing device 110.

Possible solutions to resolve the issue can also be determined and/orincluded in the user interface rendered and/or displayed by the clientcomputing device 110. The possible solutions may be to reverse actionsidentified as being possible causes, suggest the installation of newhardware to replace failing or failed hardware, suggest new times tobackup or restore a deduplication database 247 (e.g., suggest times thatdo not conflict with usual times in which deduplication pruningoperations occur), suggest new times to initiate deduplication pruningoperations (e.g., suggest times that do not conflict with usual timesthat secondary copy or restore operations occur), and/or the like.

At block 1510, one or more graphs corresponding to the anomalousprocessing and/or generation times is generated. The graph(s) can bedisplayed in a user interface rendered and/or displayed by a clientcomputing device 110. The graph(s) can depict the time to processarchive files to be deleted and/or the time to generate a list ofarchive files or chunks to delete at different time instants over aperiod of time, with markings or annotations indicating which timeinstants correspond to anomalous times and/or which deduplicationpruning operations are anomalous.

At block 1512, an alert that includes the generated graph(s) isgenerated. In addition to the graph(s), the alert can include a tablewith deduplication pruning information, acceptable ranges and/or rangesthat were violated, and/or other visual or non-visual information.

Optionally, a filtering operation can be performed before an alert isgenerated. The filtering operation may result in no alert beinggenerated even if the time to process archive files to be deleted and/orthe time to generate a list of archive files or chunks to delete falloutside the acceptable ranges.

In some embodiments, a user or administrator can indicate that an alertshould be silenced or that an alert is not wanted, in which case thealert may not be generated. After the alert is generated, the method1500 is complete.

In regard to the figures described herein, other embodiments arepossible, such that the above-recited components, steps, blocks,operations, and/or messages/requests/queries/instructions aredifferently arranged, sequenced, sub-divided, organized, and/orcombined. In some embodiments, a different component may initiate orexecute a given operation. For example, in some embodiments, the anomalydetection and reporting functionality described herein as beingperformed by the storage manager 140 can be performed by a componentexternal to the storage manager 140, not shown, in place of or inconjunction with the storage manager 140. The external component canstore event and/or jobs data, and/or retrieve such data from the storagemanager 140, the client computing device(s) 110, the secondary storagecomputing device(s) 106, and/or the like.

Example Embodiments

Some example enumerated embodiments are recited in this section in theform of methods, systems, and non-transitory computer-readable media,without limitation.

One aspect of the disclosure provides a networked information managementsystem comprising a client computing device having one or more firsthardware processors, where a first type of event occurs on the clientcomputing device. The networked information management system furthercomprises one or more computing devices in communication with the clientcomputing device, where the one or more computing devices are configuredwith computer-executable instructions that, when executed, cause the oneor more computing devices to: retrieve event data corresponding to thefirst type of event and the client computing device; perform atime-series decomposition of the event data; analyze a component of thedecomposed time-series to determine an acceptable range for a number ofoccurrences of the first type of event; determine not to expand theacceptable range in response to an indication that a number of alertsgenerated for the first type of event is less than a threshold;determine that an anomaly exists at a first time in response to adetermination that a number of occurrences of the first type of eventfalls outside the acceptable range; and generate an alert for thedetected anomaly.

The networked information management system of the preceding paragraphcan include any sub-combination of the following features: where thecomputer-executable instructions, when executed, further cause the oneor more computing devices to perform the time-series decomposition ofthe event data to form a trend component, a seasonal component, and anerror component; where the computer-executable instructions, whenexecuted, further cause the one or more computing devices to analyze theerror component to determine the acceptable range for the number ofoccurrences of the first type of event; where the computer-executableinstructions, when executed, further cause the one or more computingdevices to determine that a second anomaly exists at the first time inresponse to a determination that a duration between occurrences of thefirst type of event falls outside a second acceptable range; where theduration between occurrences of the first type of event is less than alower extreme of the second acceptable range; where the number ofoccurrences of the first type of event is greater than an upper limit ofthe acceptable range; and where the number of occurrences of the firsttype of event is less than a lower limit of the acceptable range.

Another aspect of the disclosure provides a computer-implemented methodcomprising: retrieving event data corresponding to a first type of eventthat occurs on a client computing device; performing a time-seriesdecomposition of the event data; analyzing a component of the decomposedtime-series to determine an acceptable range for a number of occurrencesof the first type of event; determining not to expand the acceptablerange in response to an indication that a number of alerts generated forthe first type of event is less than a threshold; determining that ananomaly exists at a first time in response to a determination that anumber of occurrences of the first type of event falls outside theacceptable range; and generating an alert for the detected anomaly.

The computer-implemented method of the preceding paragraph can includeany sub-combination of the following features: where performing thetime-series decomposition further comprises performing the time-seriesdecomposition of the event data to form a trend component, a seasonalcomponent, and an error component; where analyzing a component of thedecomposed time-series further comprises analyzing the error componentto determine the acceptable range for the number of occurrences of thefirst type of event; where the computer-implemented method furthercomprises determining that a second anomaly exists at the first time inresponse to a determination that a duration between occurrences of thefirst type of event falls outside a second acceptable range; where theduration between occurrences of the first type of event is less than alower extreme of the second acceptable range; where the number ofoccurrences of the first type of event is greater than an upper limit ofthe acceptable range; and where the number of occurrences of the firsttype of event is less than a lower limit of the acceptable range.

Another aspect of the disclosure provides non-transitorycomputer-readable medium storing instructions, which when executed byone or more computing devices, cause the one or more computing devicesto perform a method comprising: retrieving event data corresponding to afirst type of event that occurs on a client computing device; performinga time-series decomposition of the event data; analyzing a component ofthe decomposed time-series to determine an acceptable range for a numberof occurrences of the first type of event; determining not to expand theacceptable range in response to an indication that a number of alertsgenerated for the first type of event is less than a threshold;determining that an anomaly exists at a first time in response to adetermination that a number of occurrences of the first type of eventfalls outside the acceptable range; and generating an alert for thedetected anomaly.

The non-transitory computer-readable medium of the preceding paragraphcan include any sub-combination of the following features: where themethod further comprises performing the time-series decomposition of theevent data to form a trend component, a seasonal component, and an errorcomponent; where the method further comprises analyzing the errorcomponent to determine the acceptable range for the number ofoccurrences of the first type of event; where the method furthercomprises determining that a second anomaly exists at the first time inresponse to a determination that a duration between occurrences of thefirst type of event falls outside a second acceptable range; where theduration between occurrences of the first type of event is less than alower extreme of the second acceptable range; and where the number ofoccurrences of the first type of event is greater than an upper limit ofthe acceptable range.

Another aspect of the disclosure provides a networked informationmanagement system comprising a client computing device having one ormore first hardware processors, where the client computing device isassociated with a first job. The networked information management systemfurther comprises one or more computing devices in communication withthe client computing device, where the one or more computing devices areconfigured with computer-executable instructions that, when executed,cause the one or more computing devices to: retrieve jobs datacorresponding to the first job and the first client computing device;perform a time-series decomposition of the jobs data; analyze acomponent of the decomposed time-series to determine at least one of anacceptable range for time to perform the first job, an acceptable rangefor a size of secondary copy data associated with the first job, or anacceptable range for a number of job attempts until the first job iscomplete; determine a possible cause for the first job running longerthan the acceptable range for time to perform the first job in responseto the first job at a first time running longer than the acceptablerange for time to perform the first job; determine whether any eventscorresponding to the first job are anomalous; and generate an alert inresponse to at least one of the first job running longer or a firstevent corresponding to the first job being anomalous.

Another aspect of the disclosure provides a computer-implemented methodcomprising: retrieving jobs data corresponding to a first job and afirst client computing device; performing a time-series decomposition ofthe jobs data; analyzing a component of the decomposed time-series todetermine at least one of an acceptable range for time to perform thefirst job, an acceptable range for a size of secondary copy dataassociated with the first job, or an acceptable range fora number of jobattempts until the first job is complete; determining a possible causefor the first job running longer than the acceptable range for time toperform the first job in response to the first job at a first timerunning longer than the acceptable range for time to perform the firstjob; determining whether any events corresponding to the first job areanomalous; and generating an alert in response to at least one of thefirst job running longer or a first event corresponding to the first jobbeing anomalous.

Another aspect of the disclosure provides a non-transitorycomputer-readable medium storing instructions, which when executed byone or more computing devices, cause the one or more computing devicesto perform a method comprising: retrieving jobs data corresponding to afirst job and a first client computing device; performing a time-seriesdecomposition of the jobs data; analyzing a component of the decomposedtime-series to determine at least one of an acceptable range for time toperform the first job, an acceptable range for a size of secondary copydata associated with the first job, or an acceptable range for a numberof job attempts until the first job is complete; determining a possiblecause for the first job running longer than the acceptable range fortime to perform the first job in response to the first job at a firsttime running longer than the acceptable range for time to perform thefirst job; determining whether any events corresponding to the first jobare anomalous; and generating an alert in response to at least one ofthe first job running longer or a first event corresponding to the firstjob being anomalous.

Another aspect of the disclosure provides a networked informationmanagement system comprising one or more client computing devices eachhaving one or more first hardware processors, where the one or moreclient computing devices are associated with a plurality of jobs. Thenetworked information management system further comprises one or morecomputing devices in communication with the client computing device,where the one or more computing devices are configured withcomputer-executable instructions that, when executed, cause the one ormore computing devices to: retrieve jobs data corresponding to theplurality of jobs; perform a time-series decomposition of the jobs data;analyze a component of the decomposed time-series to determine at leastone of an acceptable range for succeeded jobs in the plurality of jobs,an acceptable range for failed jobs in the plurality of jobs, anacceptable range for killed jobs in the plurality of jobs, an acceptablerange for suspended jobs in the plurality of jobs, or an acceptablerange for pending jobs in the plurality of jobs; determine a possiblecause for at least one of the succeeded jobs, the failed jobs, thekilled jobs, the suspended jobs, or the pending jobs falling outside therespective acceptable range; generate a graph corresponding to a numberof succeeded jobs, failed jobs, killed jobs, suspended jobs, and pendingjobs; and generate an alert indicating an anomalous status of at leastone of the succeeded jobs, the failed jobs, the killed jobs, thesuspended jobs, or the pending jobs, where the alert includes thegenerated graph.

Another aspect of the disclosure provides a computer-implemented methodcomprising: retrieving jobs data corresponding to a plurality of jobs;performing a time-series decomposition of the jobs data; analyzing acomponent of the decomposed time-series to determine at least one of anacceptable range for succeeded jobs in the plurality of jobs, anacceptable range for failed jobs in the plurality of jobs, an acceptablerange for killed jobs in the plurality of jobs, an acceptable range forsuspended jobs in the plurality of jobs, or an acceptable range forpending jobs in the plurality of jobs; determining a possible cause forat least one of the succeeded jobs, the failed jobs, the killed jobs,the suspended jobs, or the pending jobs falling outside the respectiveacceptable range; generating a graph corresponding to a number ofsucceeded jobs, failed jobs, killed jobs, suspended jobs, and pendingjobs; and generating an alert indicating an anomalous status of at leastone of the succeeded jobs, the failed jobs, the killed jobs, thesuspended jobs, or the pending jobs, where the alert includes thegenerated graph.

Another aspect of the disclosure provides a non-transitorycomputer-readable medium storing instructions, which when executed byone or more computing devices, cause the one or more computing devicesto perform a method comprising: retrieving jobs data corresponding to aplurality of jobs; performing a time-series decomposition of the jobsdata; analyzing a component of the decomposed time-series to determineat least one of an acceptable range for succeeded jobs in the pluralityof jobs, an acceptable range for failed jobs in the plurality of jobs,an acceptable range for killed jobs in the plurality of jobs, anacceptable range for suspended jobs in the plurality of jobs, or anacceptable range for pending jobs in the plurality of jobs; determininga possible cause for at least one of the succeeded jobs, the failedjobs, the killed jobs, the suspended jobs, or the pending jobs fallingoutside the respective acceptable range; generating a graphcorresponding to a number of succeeded jobs, failed jobs, killed jobs,suspended jobs, and pending jobs; and generating an alert indicating ananomalous status of at least one of the succeeded jobs, the failed jobs,the killed jobs, the suspended jobs, or the pending jobs, where thealert includes the generated graph.

Another aspect of the disclosure provides a networked informationmanagement system comprising a deduplication database. The networkedinformation management system further comprises one or more computingdevices in communication with the deduplication database, where the oneor more computing devices are configured with computer-executableinstructions that, when executed, cause the one or more computingdevices to: retrieve deduplication pruning information associated withthe deduplication database; perform a time-series decomposition of thededuplication pruning information; analyze a component of the decomposedtime-series to determine an acceptable range for a time to processarchive files to be deleted; determine that an anomaly exists at a firsttime in response to a determination that a time to process archive filesto be deleted at the first time falls outside the acceptable range; andgenerate an alert for the detected anomaly.

The networked information management system of the preceding paragraphcan include any sub-combination of the following features: where thecomputer-executable instructions, when executed, further cause the oneor more computing devices to perform the time-series decomposition ofthe deduplication pruning information to form a trend component, aseasonal component, and an error component; where thecomputer-executable instructions, when executed, further cause the oneor more computing devices to analyze the error component to determinethe acceptable range for the time to process archive files to bedeleted; where the computer-executable instructions, when executed,further cause the one or more computing devices to analyze the componentof the decomposed time-series to determine a second acceptable range fora time to generate a list of archive files to delete; where theacceptable range comprises one or an absolute time value or a delta timevalue; where the time to process archive files to be deleted at thefirst time is greater than an upper limit of the acceptable range; wherethe time to process archive files to be deleted at the first time isless than a lower limit of the acceptable range; where thecomputer-executable instructions, when executed, further cause the oneor more computing devices to generate a graph indicating the anomaly fordisplay in a user interface rendered by a client computing device; wherethe computer-executable instructions, when executed, further cause theone or more computing devices to determine a possible cause of theanomaly in response to the determination that the anomaly exists; wherethe computer-executable instructions, when executed, further cause theone or more computing devices to determine a possible solution toresolve the anomaly based on the determined possible cause; and wherethe deduplication pruning information comprises at least one of a countof a number of archive files that the deduplication database has yet toprocess to update a table, a count of a number of archive filesidentified as only having data blocks for which a reference count iszero, or a time since a last list of archive files that only includedata blocks for which the reference count is zero was generated by thededuplication database.

Another aspect of the disclosure provides a computer-implemented methodcomprising: retrieving deduplication pruning information associated witha deduplication database; performing a time-series decomposition of thededuplication pruning information; analyzing a component of thedecomposed time-series to determine an acceptable range for a time toprocess archive files to be deleted; determining that an anomaly existsat a first time in response to a determination that a time to processarchive files to be deleted at the first time falls outside theacceptable range; and generating an alert for the detected anomaly.

The computer-implemented method of the preceding paragraph can includeany sub-combination of the following features: where performing atime-series decomposition further comprises performing the time-seriesdecomposition of the deduplication pruning information to form a trendcomponent, a seasonal component, and an error component; where analyzinga component of the decomposed time-series further comprises analyzingthe error component to determine the acceptable range for the time toprocess archive files to be deleted; where the computer-implementedmethod further comprises analyzing the component of the decomposedtime-series to determine a second acceptable range for a time togenerate a list of archive files to delete; where the acceptable rangecomprises one or an absolute time value or a delta time value; where thetime to process archive files to be deleted at the first time is greaterthan an upper limit of the acceptable range; where the time to processarchive files to be deleted at the first time is less than a lower limitof the acceptable range; where the computer-implemented method furthercomprises determining a possible cause of the anomaly in response to thedetermination that the anomaly exists, and determining a possiblesolution to resolve the anomaly based on the determined possible cause;and where the deduplication pruning information comprises at least oneof a count of a number of archive files that the deduplication databasehas yet to process to update a table, a count of a number of archivefiles identified as only having data blocks for which a reference countis zero, or a time since a last list of archive files that only includedata blocks for which the reference count is zero was generated by thededuplication database.

In other embodiments, a system or systems may operate according to oneor more of the methods and/or computer-readable media recited in thepreceding paragraphs. In yet other embodiments, a method or methods mayoperate according to one or more of the systems and/or computer-readablemedia recited in the preceding paragraphs. In yet more embodiments, acomputer-readable medium or media, excluding transitory propagatingsignals, may cause one or more computing devices having one or moreprocessors and non-transitory computer-readable memory to operateaccording to one or more of the systems and/or methods recited in thepreceding paragraphs.

Terminology

Conditional language, such as, among others, “can,” “could,” “might,” or“may,” unless specifically stated otherwise, or otherwise understoodwithin the context as used, is generally intended to convey that certainembodiments include, while other embodiments do not include, certainfeatures, elements and/or steps. Thus, such conditional language is notgenerally intended to imply that features, elements and/or steps are inany way required for one or more embodiments or that one or moreembodiments necessarily include logic for deciding, with or without userinput or prompting, whether these features, elements and/or steps areincluded or are to be performed in any particular embodiment.

Unless the context clearly requires otherwise, throughout thedescription and the claims, the words “comprise,” “comprising,” and thelike are to be construed in an inclusive sense, as opposed to anexclusive or exhaustive sense, i.e., in the sense of “including, but notlimited to.” As used herein, the terms “connected,” “coupled,” or anyvariant thereof means any connection or coupling, either direct orindirect, between two or more elements; the coupling or connectionbetween the elements can be physical, logical, or a combination thereof.Additionally, the words “herein,” “above,” “below,” and words of similarimport, when used in this application, refer to this application as awhole and not to any particular portions of this application. Where thecontext permits, words using the singular or plural number may alsoinclude the plural or singular number respectively. The word “or” inreference to a list of two or more items, covers all of the followinginterpretations of the word: any one of the items in the list, all ofthe items in the list, and any combination of the items in the list.Likewise the term “and/or” in reference to a list of two or more items,covers all of the following interpretations of the word: any one of theitems in the list, all of the items in the list, and any combination ofthe items in the list.

In some embodiments, certain operations, acts, events, or functions ofany of the algorithms described herein can be performed in a differentsequence, can be added, merged, or left out altogether (e.g., not allare necessary for the practice of the algorithms). In certainembodiments, operations, acts, functions, or events can be performedconcurrently, e.g., through multi-threaded processing, interruptprocessing, or multiple processors or processor cores or on otherparallel architectures, rather than sequentially.

Systems and modules described herein may comprise software, firmware,hardware, or any combination(s) of software, firmware, or hardwaresuitable for the purposes described. Software and other modules mayreside and execute on servers, workstations, personal computers,computerized tablets, PDAs, and other computing devices suitable for thepurposes described herein. Software and other modules may be accessiblevia local computer memory, via a network, via a browser, or via othermeans suitable for the purposes described herein. Data structuresdescribed herein may comprise computer files, variables, programmingarrays, programming structures, or any electronic information storageschemes or methods, or any combinations thereof, suitable for thepurposes described herein. User interface elements described herein maycomprise elements from graphical user interfaces, interactive voiceresponse, command line interfaces, and other suitable interfaces.

Further, processing of the various components of the illustrated systemscan be distributed across multiple machines, networks, and othercomputing resources. Two or more components of a system can be combinedinto fewer components. Various components of the illustrated systems canbe implemented in one or more virtual machines, rather than in dedicatedcomputer hardware systems and/or computing devices. Likewise, the datarepositories shown can represent physical and/or logical data storage,including, e.g., storage area networks or other distributed storagesystems. Moreover, in some embodiments the connections between thecomponents shown represent possible paths of data flow, rather thanactual connections between hardware. While some examples of possibleconnections are shown, any of the subset of the components shown cancommunicate with any other subset of components in variousimplementations.

Embodiments are also described above with reference to flow chartillustrations and/or block diagrams of methods, apparatus (systems) andcomputer program products. Each block of the flow chart illustrationsand/or block diagrams, and combinations of blocks in the flow chartillustrations and/or block diagrams, may be implemented by computerprogram instructions. Such instructions may be provided to a processorof a general purpose computer, special purpose computer,specially-equipped computer (e.g., comprising a high-performancedatabase server, a graphics subsystem, etc.) or other programmable dataprocessing apparatus to produce a machine, such that the instructions,which execute via the processor(s) of the computer or other programmabledata processing apparatus, create means for implementing the actsspecified in the flow chart and/or block diagram block or blocks. Thesecomputer program instructions may also be stored in a non-transitorycomputer-readable memory that can direct a computer or otherprogrammable data processing apparatus to operate in a particularmanner, such that the instructions stored in the computer-readablememory produce an article of manufacture including instruction meanswhich implement the acts specified in the flow chart and/or blockdiagram block or blocks. The computer program instructions may also beloaded to a computing device or other programmable data processingapparatus to cause operations to be performed on the computing device orother programmable apparatus to produce a computer implemented processsuch that the instructions which execute on the computing device orother programmable apparatus provide steps for implementing the actsspecified in the flow chart and/or block diagram block or blocks.

Any patents and applications and other references noted above, includingany that may be listed in accompanying filing papers, are incorporatedherein by reference. Aspects of one or more embodiments can be modified,if necessary, to employ the systems, functions, and concepts of thevarious references described above. These and other changes can be madein light of the above Detailed Description. While the above descriptiondescribes certain examples, and describes the best mode contemplated, nomatter how detailed the above appears in text, different embodiments canbe practiced in many ways. Details of the system may vary considerablyin its specific implementation. As noted above, particular terminologyused when describing certain features should not be taken to imply thatthe terminology is being redefined herein to be restricted to anyspecific characteristics, features with which that terminology isassociated. In general, the terms used in the following claims shouldnot be construed to limit the scope the specific examples disclosed inthe specification, unless the above Detailed Description sectionexplicitly defines such terms. Accordingly, the actual scope encompassesnot only the disclosed examples, but also all equivalent ways ofpracticing or implementing the claims.

To reduce the number of claims, certain aspects are presented below incertain claim forms, but the applicant contemplates other aspects in anynumber of claim forms. For example, while only one aspect may be recitedas a means-plus-function claim under 35 U.S.C. sec. 112(f) (AIA), otheraspects may likewise be embodied as a means-plus-function claim, or inother forms, such as being embodied in a computer-readable medium. Anyclaims intended to be treated under 35 U.S.C. § 112(f) will begin withthe words “means for,” but use of the term “for” in any other context isnot intended to invoke treatment under 35 U.S.C. § 112(f). Accordingly,the applicant reserves the right to pursue additional claims afterfiling this application, in either this application or in a continuingapplication.

What is claimed is:
 1. A networked information management systemcomprising: a client computing device having one or more first hardwareprocessors, wherein the client computing device is associated with afirst job; and one or more computing devices in communication with theclient computing device, wherein the one or more computing devices areconfigured with computer-executable instructions that, when executed,cause the one or more computing devices to: retrieve jobs datacorresponding to the first job and the first client computing device;perform a time-series decomposition of the jobs data; analyze acomponent of the decomposed time-series to determine at least one of anacceptable range for time to perform the first job, an acceptable rangefor a size of secondary copy data associated with the first job, or anacceptable range for a number of job attempts until the first job iscomplete; determine a possible cause for the first job running longerthan the acceptable range for time to perform the first job in responseto the first job at a first time running longer than the acceptablerange for time to perform the first job; determine whether any eventscorresponding to the first job are anomalous; and generate an alertregarding an issue with the networked information management system inresponse to at least one of the first job running longer or a firstevent corresponding to the first job being anomalous.
 2. The networkedinformation management system of claim 1, wherein thecomputer-executable instructions, when executed, further cause the oneor more computing devices to perform the time-series decomposition ofthe jobs data to form a trend component, a seasonal component, and anerror component.
 3. The networked information management system of claim2, wherein the computer-executable instructions, when executed, furthercause the one or more computing devices to analyze the error componentto determine at least one of the acceptable range for time to performthe first job, the acceptable range for the size of the secondary copydata associated with the first job, or the acceptable range for thenumber of job attempts until the first job is complete.
 4. The networkedinformation management system of claim 1, wherein the jobs datacomprises at least one of historical data indicating a job length,secondary copy data size, or a number of job attempts for performing thefirst job at different time instants over a period of time.
 5. Thenetworked information management system of claim 1, wherein the firstjob comprises an incremental backup job or a full backup job.
 6. Thenetworked information management system of claim 1, wherein the possiblecause comprises one of an activity being disabled, a secondary copyoperation window not being enforced, a user suspending the first job,content of the secondary copy data being processed by the first jobchanging, or the size of the secondary copy data increasing.
 7. Thenetworked information management system of claim 1, wherein first eventcomprises an event that occurs as a result of one of an action taken bythe first job or a status of the first job changing.
 8. The networkedinformation management system of claim 1, wherein thecomputer-executable instructions, when executed, further cause the oneor more computing devices to perform a filter operation prior togeneration of the alert.
 9. A computer-implemented method comprising:retrieving jobs data corresponding to a first job and a first clientcomputing device; performing a time-series decomposition of the jobsdata; analyzing a component of the decomposed time-series to determineat least one of an acceptable range for time to perform the first job,an acceptable range for a size of secondary copy data associated withthe first job, or an acceptable range for a number of job attempts untilthe first job is complete; determining a possible cause for the firstjob running longer than the acceptable range for time to perform thefirst job in response to the first job at a first time running longerthan the acceptable range for time to perform the first job; determiningwhether any events corresponding to the first job are anomalous; andgenerating an alert regarding an issue with a networked informationmanagement system in response to at least one of the first job runninglonger or a first event corresponding to the first job being anomalous.10. The computer-implemented method of claim 9, wherein performing atime-series decomposition further comprises performing the time-seriesdecomposition of the jobs data to form a trend component, a seasonalcomponent, and an error component.
 11. The computer-implemented methodof claim 10, wherein analyzing a component of the decomposed time-seriesfurther comprises analyzing the error component to determine at leastone of the acceptable range for time to perform the first job, theacceptable range for the size of the secondary copy data associated withthe first job, or the acceptable range for the number of job attemptsuntil the first job is complete.
 12. The computer-implemented method ofclaim 9, wherein the jobs data comprises at least one of historical dataindicating a job length, secondary copy data size, or a number of jobattempts for performing the first job at different time instants over aperiod of time.
 13. The computer-implemented method of claim 9, whereinthe first job comprises an incremental backup job or a full backup job.14. The computer-implemented method of claim 9, wherein the possiblecause comprises one of an activity being disabled, a secondary copyoperation window not being enforced, a user suspending the first job,content of the secondary copy data being processed by the first jobchanging, or the size of the secondary copy data increasing.
 15. Thecomputer-implemented method of claim 9, wherein first event comprises anevent that occurs as a result of one of an action taken by the first jobor a status of the first job changing.
 16. The computer-implementedmethod of claim 9, further comprising performing a filter operationprior to generation of the alert.
 17. A non-transitory computer-readablemedium storing instructions, which when executed by one or morecomputing devices, cause the one or more computing devices to perform amethod comprising: retrieving jobs data corresponding to a first job anda first client computing device; performing a time-series decompositionof the jobs data; analyzing a component of the decomposed time-series todetermine at least one of an acceptable range for time to perform thefirst job, an acceptable range for a size of secondary copy dataassociated with the first job, or an acceptable range for a number ofjob attempts until the first job is complete; determining a possiblecause for the first job running longer than the acceptable range fortime to perform the first job in response to the first job at a firsttime running longer than the acceptable range for time to perform thefirst job; determining whether any events corresponding to the first jobare anomalous; and generating an alert regarding an issue with anetworked information management system in response to at least one ofthe first job running longer or a first event corresponding to the firstjob being anomalous.
 18. The non-transitory computer-readable medium ofclaim 17, wherein the instructions, when executed by the one or morecomputing devices, further cause the one or more computing devices toperform a method comprising performing the time-series decomposition ofthe jobs data to form a trend component, a seasonal component, and anerror component.
 19. The non-transitory computer-readable medium ofclaim 18, wherein the instructions, when executed by the one or morecomputing devices, further cause the one or more computing devices toperform a method comprising analyzing the error component to determineat least one of the acceptable range for time to perform the first job,the acceptable range for the size of the secondary copy data associatedwith the first job, or the acceptable range for the number of jobattempts until the first job is complete.
 20. The non-transitorycomputer-readable medium of claim 17, wherein the instructions, whenexecuted by the one or more computing devices, further cause the one ormore computing devices to perform a method comprising performing afilter operation prior to generation of the alert.